Programa de Pós-Graduação em Estatística

Produção Científica do Período de 2008 a 2025

Apresentações de Trabalho

Número total de items: 414

2025

  1. MATOS, L. A. Modeling Longitudinal Data using Robust Mixed Models. 2025.
    [ Google | Google Scholar ]
  2. Garcia, Nancy L. Pós-Graduação em Matemática/Probabilidade e Estatística, passado, presente e futuro. 2025.
    [ Google | Google Scholar ]

2024

  1. SOUSA, A. R. S. Sobre a estimação Bayesiana de coeficientes de ondaletas em modelos de regressão não paramétrica com erros estacionários. 2024.
    [ Google | Google Scholar ]
  2. TRUCÍOS, CARLOS A new proposal to forecast high-dimensional conditional covariance matrices via general dynamic factor models.. 2024.
    [ Google | Google Scholar ]
  3. MATOS, L. A. Extending multivariate Student's-t semiparametric mixed models for longitudinal data with censored responses and heavy tails. 2024.
    [ Google | Google Scholar ]
  4. Motta, M. R.; GARCIA, NANCY L.; MIGON, H.; PETKOVA, E.; TARPEY, T.; OGDEN, R. T. Unsupervised Bayesian Classification for Mixture Regression Models with Scalar and Functional Covariates. 2024.
    [ Google | Google Scholar ]
  5. Motta, M. R.; FORKMAN, J. Bayesian Analysis of Nonnegative Data Using Dependency-Extended Two-Part Models. 2024.
    [ Google | Google Scholar ]
  6. AONO, A. H.; Motta, M. R.; SOUZA, A. P. OPTIMIZING GUINEA GRASS (Megathyrsus maximus) BREEDING THROUGH GENOMIC SELECTION. 2024.
    [ Google | Google Scholar ]
  7. FERNANDES, F. A.; Motta, M. R. Regressão Não Linear Quantílica para Modelagem de Crescimento de Frangos Machos,. 2024.
    [ Google | Google Scholar ]
  8. Motta, M. R.; Garcia, N. L. CORRELATED FUNCTIONAL DATA. 2024.
    [ Google | Google Scholar ]
  9. Garcia, Nancy L. Da escola da Fazendo Palmeiras até a Eleição para a Academia Brasileira de Ciências. 2024.
    [ Google | Google Scholar ]
  10. Garcia, Nancy L. Identificação de respondentes a placebo baseado em covariáveis e sinais de EEG (e mais, se der tempo). 2024.
    [ Google | Google Scholar ]
  11. Garcia, Nancy L. Statistics - palying into everyone's backyard. 2024.
    [ Google | Google Scholar ]
  12. Garcia, Nancy L.; MOTTA, M. R.; MIGON, H.; PETKOVA, E.; TARPEY, T.; OGDEN, R. T. Identificação de respondentes a placebo baseado em covariáveis e sinais de EEG (e mais, se der tempo). 2024.
    [ Google | Google Scholar ]
  13. Garcia, Nancy L. Statistics - playing into everyone's backyard. 2024.
    [ Google | Google Scholar ]
  14. CARVALHO, D. A. F.; MAIA, R.P. Modelo de regressão t assimétrica Tipo II com efeitos aleatórios aplicado à análise longitudinal de desempenho acadêmico. 2024.
    [ Google | Google Scholar ]
  15. MAIA, R. P.; GAONA, N. B. R. Analisando dados de desempenho acadêmico o tempo de permanência via um modelo conjunto de dados longitudinais e de sobrevivência com tempo discreto. 2024.
    [ Google | Google Scholar ]
  16. GARCÍA, JESÚS E.; González-López, V.A. The Efficient Determination Criterion for Partition Markov Model Estimation. 2024.
    [ Google | Google Scholar ]
  17. González López, V. A.; LITVINOFF JUSTUS, VINÍCIUS An Examination of a Necessary and Sufficient Condition for Conditional Independence. 2024.
    [ Google | Google Scholar ]
  18. GARCÍA, JESÚS E.; González-López, V.A.; Sanchez, J. G. A metric based on the efficient determination criterion. 2024.
    [ Google | Google Scholar ]

2023

  1. SOUSA, A. R. S. Regras bayesianas de encolhimento de coeficientes de ondaletas em modelos com erros gaussianos e positivos. 2023.
    [ Google | Google Scholar ]
  2. CARVALHO, BENILTON S; SOUZA, W.; CENDES, I. T. L. RNNR: distributed task execution system for scaling reproducible workflows (pôster). 2023.
    [ Google | Google Scholar ]
  3. CARVALHO, BENILTON S Do Código à Cura Ciência de Dados e Condições Neurológicas. 2023.
    [ Google | Google Scholar ]
  4. CARVALHO, BENILTON S Precision Medicine in Brazil. 2023.
    [ Google | Google Scholar ]
  5. AZEVEDO, C. L. N. Um convite à Teoria da Resposta ao Item (TRI). 2023.
    [ Google | Google Scholar ]
  6. TRUCÍOS, CARLOS; HALLIN, M. Forecasting risk measures in high-dimensional portfolios. 2023.
    [ Google | Google Scholar ]
  7. TRUCÍOS, CARLOS Forecasting conditional covariance matrices in high-dimensional time series via general dynamic factor models: portfolio allocation and risk measures. 2023.
    [ Google | Google Scholar ]
  8. TRUCÍOS, CARLOS Forecasting risk measures in high dimensional portfolios. 2023.
    [ Google | Google Scholar ]
  9. TRUCÍOS, CARLOS Does portfolio resampling really improve out-of-sample performance? Evidence from the Brazilian and US markets. 2023.
    [ Google | Google Scholar ]
  10. TRUCÍOS, CARLOS Forecasting conditional covariance matrices in high-dimensional time series via general dynamic factor models: portfolio allocation and risk measures. 2023.
    [ Google | Google Scholar ]
  11. TRUCÍOS, CARLOS Forecasting Value-at-Risk and Expected Shortfall in large portfolios. 2023.
    [ Google | Google Scholar ]
  12. TRUCÍOS, CARLOS High-Dimensional Financial Time Series. 2023.
    [ Google | Google Scholar ]
  13. TRUCÍOS, CARLOS Forecasting risk measures in high-dimensional portfolios with additive outliers. 2023.
    [ Google | Google Scholar ]
  14. TRUCÍOS, CARLOS Forecasting conditional covariance matrices in high-dimensional time series via general dynamic factor models: portfolio allocation and risk measures. 2023.
    [ Google | Google Scholar ]
  15. Pinheiro, Hildete; BARRETO, F. H. S. Evaluating academic performance using nonparametric regression. 2023.
    [ Google | Google Scholar ]
  16. GUELLA, J. Hilbert space embeddings of independence tests in 2 and several variables. 2023.
    [ Google | Google Scholar ]
  17. GUELLA, J. Testes de independência total e parcial imersos em espaços de Hilbert. 2023.
    [ Google | Google Scholar ]
  18. RODRIGUES-Motta, M. Unsupervised Bayesian classification for mixed regression models with scalar and functional covariates. 2023.
    [ Google | Google Scholar ]
  19. Rodrigues-Motta, M. Bayesian analysis of nonnegative data using dependency-extended two-part models. 2023.
    [ Google | Google Scholar ]
  20. RODRIGUES-MOTTA, MARIANA Using complex models in the identification of sick cows. 2023.
    [ Google | Google Scholar ]
  21. RODRIGUES-MOTTA, MARIANA Unsupervised Bayesian classification for mixed regression models with scalar and functional covariates. 2023.
    [ Google | Google Scholar ]
  22. FREGUGLIA, V.; GARCIA, N. L. Detecting Renewal States in texts of Brazilian and European Portuguese. 2023.
    [ Google | Google Scholar ]
  23. Garcia, Nancy L. Statistics: a fundamental tool for analysing data. 2023.
    [ Google | Google Scholar ]
  24. Garcia, Nancy L.; ROCHA, M.; ZAMBOM, A. Z.; KIM, S. 'Variable length Markov chain with exogenous covariates'. 2023.
    [ Google | Google Scholar ]
  25. Garcia, Nancy L.; MEDEIROS, C. B. Mesa redonda: Open Science. 2023.
    [ Google | Google Scholar ]
  26. Garcia, Nancy L. Estatística: uma ferramenta fundamental para problemas aplicados. 2023.
    [ Google | Google Scholar ]
  27. García, Jesús E.; González-López, V.A.; Sanchez, J. G. A Metric Based on the EDC Criterion. 2023.
    [ Google | Google Scholar ]
  28. García, Jesús E.; González-López, V. A.; Sanchez, J. G. Quantifying Errors in Minimal Partition Identification through the Efficient Determination Criterion. 2023.
    [ Google | Google Scholar ]
  29. González López, V. A. Data Science and Statistics: Challenges and Opportunities in Latin America (In Data Science And Statistics: Challenges And Opportunities Within The ISI). 2023.
    [ Google | Google Scholar ]
  30. González López, V. A. Building a Metric through the Efficient Determination Criterion. 2023.
    [ Google | Google Scholar ]
  31. González-López, V.A. Representation of the dependence among indicators arising from reservoirs/tailing dams breakout. 2023.
    [ Google | Google Scholar ]

2022

  1. SOUSA, A. R. S. Testes Estatísticos em Aprendizagem de Máquinas. 2022.
    [ Google | Google Scholar ]
  2. GALINDO, A. F. L.; AZEVEDO, C. L. N.; Nobre, J. S. Semi-parametric models for independent and longitudinal data with limited response. 2022.
    [ Google | Google Scholar ]
  3. FREITAS, J. V. B.; AZEVEDO, C. L. N.; Nobre, J. S. Semi-parametric models based on the Scale Mixtures of Centred Skew Normal distribution for independent and longitudinal data. 2022.
    [ Google | Google Scholar ]
  4. TRUCÍOS, CARLOS Forecasting Conditional Covariance Matrices in High- Dimentional Time Series: Implications for Portfolio Allocation.. 2022.
    [ Google | Google Scholar ]
  5. TRUCÍOS, CARLOS Modelando Series Temporales Financieras en Alta Dimensión.. 2022.
    [ Google | Google Scholar ]
  6. TRUCÍOS, CARLOS A Comparison of Methods for Forecasting Value-at-Risk and Expected Shortfall of Cryptocurrencies. 2022.
    [ Google | Google Scholar ]
  7. TRUCÍOS, CARLOS Risk Measures in Cryptocurrency Markets. 2022.
    [ Google | Google Scholar ]
  8. TRUCÍOS, CARLOS Academia, mercado e otras cositas mas.. 2022.
    [ Google | Google Scholar ]
  9. TRUCÍOS, CARLOS Academia, mercado e otras cositas mas. 2022.
    [ Google | Google Scholar ]
  10. Vilca, F. Influence analysis and outlier tests for log-Birnbaum-Saunders regression models based on the EM algorithm. 2022.
    [ Google | Google Scholar ]
  11. Vilca, F. Diagnostic analysis based on the EM algorithm: Application for Birnbaum-Saunders regression models. 2022.
    [ Google | Google Scholar ]
  12. PINHEIRO, H. P.; Farias, B.M. The use of quasi U-statistics to test homogeneity of variants of coronavirus. 2022.
    [ Google | Google Scholar ]
  13. GUELLA, J. C. SOME RECENT THEORETICAL RESULTS ABOUT HILBERT SPACE EMBEDDINGS OF PROBABILITIES.. 2022.
    [ Google | Google Scholar ]
  14. GUELLA, J. Recent theoretical results on MMD, Energy distance, HSIC and its generalizations. 2022.
    [ Google | Google Scholar ]
  15. GUELLA, J. C. Recent theoretical results about Hilbert space embeddings of probabilities.. 2022.
    [ Google | Google Scholar ]
  16. Freitas, J. V. B.; AZEVEDO, C. L. N.; Nobre, J.S. Semi-parametric models based on the Scale Mixtures of CentredSkew Normal distribution for independent and longitudinal data. 2022.
    [ Google | Google Scholar ]
  17. Bessa, P. V. O.; Rocha, E. O.; BATISTA, M. J.; Nobre, J.S. Método Bootstrap para estimação Intervalar. 2022.
    [ Google | Google Scholar ]
  18. MATOS, L. A. Extending multivariate Student's-t semiparametric mixed models for longitudinal data with censored responses and heavy tails. 2022.
    [ Google | Google Scholar ]
  19. MATOS, L. A. Tutorial: Modeling Longitudinal Data using Robust Mixed Models in R. 2022.
    [ Google | Google Scholar ]
  20. Motta, M. R. Unsupervised Bayesian classification for models with scalar and functional covariates. 2022.
    [ Google | Google Scholar ]
  21. Rodrigues-Motta, M. Unsupervised Bayesian classification for mixed regression models with scalar and functional covariates. 2022.
    [ Google | Google Scholar ]
  22. ZEVALLOS, Mauricio Minimum Distance Estimation of Long-Memory Stochastic Duration Models. 2022.
    [ Google | Google Scholar ]
  23. Garcia, Nancy L.; SILVA, D. D. B. N. How can Statistics be used to address global challenges?. 2022.
    [ Google | Google Scholar ]
  24. Garcia, Nancy L.; MOTTA, M. R.; MIGON, H.; GIORDANO, J.; PEREZ, M. M. Unsupervised Bayesian classification for mixed regression models with applications to predicting illness for milking cows. 2022.
    [ Google | Google Scholar ]
  25. FREGUGLIA, V.; Garcia, Nancy L. Bayesian analysis of Brazilian and European written texts. 2022.
    [ Google | Google Scholar ]
  26. FREGUGLIA, V.; Garcia, Nancy L. Detecting Renewal States in Chains of Variable Length via Intrinsic Bayes Factors. 2022.
    [ Google | Google Scholar ]
  27. GARCIA, N. L.; MOTTA, M. R.; MIGON, H.; GIORDANO, J.; PEREZ, M. M. Unsupervised Bayesian classification for mixed regression models with applications to predicting illness for milking cows. 2022.
    [ Google | Google Scholar ]
  28. GARCIA, N. L.; FREGUGLIA, V. Análise Bayesiana de Textos Escritos de Português Europeu Moderno e Brasileiro. 2022.
    [ Google | Google Scholar ]
  29. Garcia, Nancy L.; FREGUGLIA, V. Bayesian analysis of Brazilian and European written texts. 2022.
    [ Google | Google Scholar ]
  30. VERGILIO, M. M.; Kiihl, S.; FLORINDO, J. B.; Freitas, L. S.; Ribeiro, M. D.; NASCIMENTO, D. C. M.; AIELLO, L. M.; LEONARDI, G. R. Machine learning in high-frequency ultrasound skin imaging for cosmetics assessment. 2022.
    [ Google | Google Scholar ]
  31. Schultz, J.; González-López, V. A. Copula Analysis of the 2018 Brazilian Presidential Election. 2022.
    [ Google | Google Scholar ]
  32. González-López, V. A. A Stochastic Characterization of Omicron Variant of SARS-COV 2 Virus. 2022.
    [ Google | Google Scholar ]
  33. García, Jesús E.; González López, V. A. A Stochastic Characterization of Omicron Variant of SARS-COV 2 Virus. 2022.
    [ Google | Google Scholar ]

2021

  1. Lebensztayn, E. Limit theorems for the Maki-Thompson rumour model. 2021.
    [ Google | Google Scholar ]
  2. Vilca, F. A Robust Birnbaum-Saunders Regression Model based on asymmetric heavy-tailed distributions. 2021.
    [ Google | Google Scholar ]
  3. Freitas, J. V. B.; Nobre, J.S.; Bourguignon, M.; SANTOS, M. F. A new approach to modeling positive random variables with repeated measures. 2021.
    [ Google | Google Scholar ]
  4. Rocha, E. O.; Bessa, P. V. O.; BATISTA, M. J.; Nobre, J.S. Modelos Lineares Generalizados: Uma aplicação de regressão Poisson em R. 2021.
    [ Google | Google Scholar ]
  5. Silva, L. P.; Nobre, J.S.; Santos, J. R. S. Análise de regressão e análise de dados multivariados via teoria de cópulas. 2021.
    [ Google | Google Scholar ]
  6. Santos Filho, D. J.; Nobre, J.S. Utilização da estatística gradiente e seu refinamento em modelos lineares mistos. 2021.
    [ Google | Google Scholar ]
  7. Silva, L. A.; Nobre, J.S. utilização do método Bootstrap em Modelos de Regressão Linear Simples. 2021.
    [ Google | Google Scholar ]
  8. ZEVALLOS, Mauricio; ABBARA, OMAR Estimation and forecasting of long-memory stochastic volatility models. 2021.
    [ Google | Google Scholar ]
  9. GALVES, A.; GALVES, C.; GARCIA, J.; GARCIA, N. L.; LEONARDI, F. Context Tree Selection and Linguistic Rhythm Retrieval from Written Texts. 2021.
    [ Google | Google Scholar ]
  10. GARCIA, N. L. Interacting cluster point process model for epidermal nerve fibers. 2021.
    [ Google | Google Scholar ]
  11. MAIA, R.P. Pesquisa com tratamento estatístico. 2021.
    [ Google | Google Scholar ]
  12. García, Jesús E.; González López, V. A.; TASCA, G. H. Comparison between Genomic Sequences of SARS-CoV 2 Virus: Original versus P.1 Variant. 2021.
    [ Google | Google Scholar ]
  13. Viola, M. L. L.; García, Jesús E.; González-López, V. A. Markovian Structure of Interaction for Three Indicators of Linguistic Rhythm. 2021.
    [ Google | Google Scholar ]
  14. González López, V. A. Covid-19 its Origins and Variants from a Stochastic Perspective. 2021.
    [ Google | Google Scholar ]
  15. González-López, V. A. Language modality representation through multiple partition Markov models. 2021.
    [ Google | Google Scholar ]
  16. García, Jesús E.; González-López, V. A.; TASCA, G. H.; SANTOS, VINÍCIUS G.; Fernandes-Svartman, F. R. Language Modality Representation through Multiple Partition Markov Models. 2021.
    [ Google | Google Scholar ]

2020

  1. CARVALHO, BENILTON S. From statistical models to medical practice: how interdisciplinarity may lead the way to excellence in health care. 2020.
    [ Google | Google Scholar ]
  2. AZEVEDO, C. L. N.; ANDRADE, D. F.; FOX, J.-P. Time series and multilevel modeling for longitudinal item response theory data. 2020.
    [ Google | Google Scholar ]
  3. ROCHA, M. G.; AZEVEDO, C. L. N. Contributions to the study of the main unfolding models of item response theory with applications in psychology. 2020.
    [ Google | Google Scholar ]
  4. AZEVEDO, C. L. N.; FOX, J.-P.; ANDRADE, D. F. Time series and multilevel modeling for longitudinal item response theory data. 2020.
    [ Google | Google Scholar ]
  5. Vilca, F. A lifetime distribution to model fatigue life on the Birnbam-Saunders distribution,. 2020.
    [ Google | Google Scholar ]
  6. Pinheiro, Hildete; PINHEIRO, A. S.; SEN, Pranab Kumar; KIIHL, Samara Flamini A nonparametric approach to assess undergraduate performance. 2020.
    [ Google | Google Scholar ]
  7. Cavalcante, J. P. L.; Nobre, J.S. Modelos de regressão beta com medida repetidas via teoria de equações de estimação generalizadas. 2020.
    [ Google | Google Scholar ]
  8. Pessoa, D. N.; Nobre, J.S.; Montalvo, G. S. A. Método de Log-cumulantes: Introdução e propriedades. 2020.
    [ Google | Google Scholar ]
  9. Santos Filho, D. J.; Nobre, J.S. Comparação entre os principais testes assintóticos. 2020.
    [ Google | Google Scholar ]
  10. MATOS, L. A. A semiparametric mixed-effects model for censored longitudinal data. 2020.
    [ Google | Google Scholar ]
  11. RODRIGUES-MOTTA, MARIANA A zero augmeted Birbaum-Saunders mixed model with repeated measures: a Bayesian approach. 2020.
    [ Google | Google Scholar ]
  12. RODRIGUES-MOTTA, MARIANA Ciclo de Palestras Grupo Elza Pesquisadoras do IMECC: Mariana Rodrigues Motta. 2020.
    [ Google | Google Scholar ]
  13. Garcia, Nancy L. A Estatística como ferramenta fundamental em problemas aplicados. 2020.
    [ Google | Google Scholar ]
  14. Garcia, Nancy L. Jobs in Brazil. 2020.
    [ Google | Google Scholar ]
  15. Garcia, Nancy L. Identificação de respondentes a placebo baseado em covariáveis e sinais de EEG. 2020.
    [ Google | Google Scholar ]
  16. MAIA, R. P. A importância da Estatística na Pesquisa Científica. 2020.
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  17. Zambom, A. Z.; COLLAZOS, JULIAN A. A.; DIAS, R. Selection of the Number of Clusters in Functional Data Analysis. 2020.
    [ Google | Google Scholar ]
  18. Benaglia, Tatiana Bayesian Mixture Models for longitudinal data on cognition loss in elderly people. 2020.
    [ Google | Google Scholar ]
  19. GARCÍA, JESÚS E.; González-López, V.A.; TASCA, GUSTAVO HENRIQUE A Stochastic Inspection about Genetic Variants of Covid-19 Circulating in Brazil during 2020. 2020.
    [ Google | Google Scholar ]
  20. González-López, V.A.; PICANCO, J. L.; Moraes, R. R. Semi-Parametric Copula-Based Estimation of Risk of Tailings Dams. 2020.
    [ Google | Google Scholar ]
  21. GARCÍA, JESÚS E.; González López, V. A. Americas Zika virus and its similarities with African and Asian lineages. 2020.
    [ Google | Google Scholar ]
  22. González-López, V.A. Full Partition Markov Models. 2020.
    [ Google | Google Scholar ]

2019

  1. AZEVEDO, C. L. N.; Vilca-Labra, F.; Lobos, C. Censored Linear Mixed Models based on the Birnbaum-Saunders distribution: Bayesian MCMC Implementation. 2019.
    [ Google | Google Scholar ]
  2. AZEVEDO, C. L. N.; Lobos, C.; Vilca-Labra, F. Birnbaum-Saunders Linear Mixed-Eects Models with Censored Data: Bayesian MCMC Inference. 2019.
    [ Google | Google Scholar ]
  3. REISEN, V. A.; AZEVEDO, C. L. N.; BONDON, P.; COTTA, H.; SARQUIZ, F. Additive mixed models with time series structures. 2019.
    [ Google | Google Scholar ]
  4. AZEVEDO, C. L. N.; Vilca-Labra, F.; Lobos, C. Censored Linear Mixed Models based on the Birnbaum-Saunders distribution: Bayesian MCMC Implementation. 2019.
    [ Google | Google Scholar ]
  5. Vilca, F. A robust multivariate Birnbaum-Saunders distribution: EM-estimation. 2019.
    [ Google | Google Scholar ]
  6. Vilca, F. Birnbaum-Saunders Linear Mixed-Effects Models with Censored Data: Bayesian MCMC Implementation. 2019.
    [ Google | Google Scholar ]
  7. BORDIN, Tatiana Buratto; PINHEIRO, H. P.; PINHEIRO, A.; REIS, S. F.; Perez, S.I. Homogeneity tests for comparisons of groups of DNA sequences using U-statistics and substitution models. 2019.
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  8. PINHEIRO, H. P.; Barbosa, E.; Benaglia, T.; Muniz-Terrera, G. Bayesian mixture models for longitudinal data on cognition loss in elderly people. 2019.
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  9. PINHEIRO, H. P.; Barbosa, E.; Benaglia, T.; Muniz-Terrera, G. Mixture models for longitudinal data of cognitive function in older adults. 2019.
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  10. BORDIN, Tatiana Buratto; Pinheiro, Hildete; PINHEIRO, A.; Perez, S.I.; REIS, S. F. Quasi U-statistics and substitution models for tests of homogeneity of groups of DNA sequences. 2019.
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  11. MATOS, L. A. Estimation for partially linear censored regression models based on heavy-tailed distributions. 2019.
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  12. MATOS, L. A. Estimation and diagnostics for partially linear censored regression models based on heavy-tailed distributions. 2019.
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  13. MATOS, L. A. Flexible longitudinal linear mixed models for multiple censored responses data. 2019.
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  14. MATOS, L. A. Longitudinal Models for Multiple Censored Responses Data. 2019.
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  15. TRUCIOS-MAZA, C. C.; Mazzeu, J. H. G.; Hallin, M.; Hotta, Luiz K.; HERÊNCIA, M. E. Z.; Valls Pereira, Pedro L. Forecasting Conditional Covariance Matrices in High-Dimensional Data: a General Dynamic Factor Approach. 2019.
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  16. TRUCÍOS, C.; Mazzeu, J. H. G.; Hallin, M.; Hotta, Luiz K.; Valls Pereira, Pedro L.; HERÊNCIA, M. E. Z. Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: a General Dynamic Factor Approach. 2019.
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  17. TRUCÍOS, C.; Mazzeu, J. H. G.; Hotta, Luiz K.; Valls Pereira, Pedro L.; Hallin, M. On the robustness of the general dynamic factor model with infinite-dimensional space: identification, estimation and forecasting. 2019.
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  18. RODRIGUES-MOTTA, MARIANA Zero inflated and zero augmented mixed models. 2019.
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  19. ZEVALLOS, Mauricio Estimation of ARFIMA models: a minimum distance approach. 2019.
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  20. ZEVALLOS, Mauricio Estimation of ARFIMA models: a minimum distance approach. 2019.
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  21. Garcia, Nancy L.; FREGUGLIA, V.; BICAS, J. Modeling textile images with hidden Gibbs random fields. 2019.
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  22. Garcia, Nancy L. Da Escola da Fazenda Palmeiras à PRPG/UNICAMP. 2019.
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  23. Garcia, Nancy L. Modeling textile images with hidden Gibbs random fields. 2019.
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  24. GARCIA, N. L. Mestres e Doutores do Século XXI. 2019.
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  25. Pinheiro, Hildete Prisco; MOTTA, M. R.; MAIA, R. P.; LIMA NETO, E. A. Zero-one augmented beta and aero-inflated discrete models with heterogeneous dispersion for the analysis od student. 2019.
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  26. MAIA, R. P.; Labouriau, R; Demétrio, CGB A discrete competing risks mixed model with masked causes: a cow longevity study. 2019.
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  27. MAIA, R. P. Histórico das políticas de ação afirmativa adotadas pela Unicamp: PAAIS, ProFIS e Cotas. 2019.
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  28. Zambom, A. Z.; COLLAZOS, JULIAN A. A.; DIAS, R. Functional Data Clustering via Hypothesis Testing K-means. 2019.
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  29. García, Jesús E.; González-López, V. A.; Silva, T. S.; Dias, D. H. Discrete Representation of an Annual Precipitation Process. 2019.
    [ Google | Google Scholar ]
  30. González-López, V.A.; Moraes, R. R. A Bayesian Copula-Based Modelling for Income Concentration and Inequality Indexes. 2019.
    [ Google | Google Scholar ]
  31. González-López, V.A. Sample Selection Procedure in Daily Trading Volume Processes. 2019.
    [ Google | Google Scholar ]
  32. González-López, V.A. Stochastic Profile of Strains of Zika from Tropical and Subtropical Regions. 2019.
    [ Google | Google Scholar ]
  33. González-López, V. A. Partition Markov Model for Multiple Processes. 2019.
    [ Google | Google Scholar ]
  34. García, Jesús E.; González-López, V.A.; Cordeiro, M. T. A.; Londoño, S. L. M. Partition Markov model for multiple processes. 2019.
    [ Google | Google Scholar ]
  35. González-López, V.A. Stochastic Profile of Strains of Zika from Tropical and Subtropical Regions. 2019.
    [ Google | Google Scholar ]

2018

  1. AZEVEDO, C.L.N.; Fox, Jean-Paul; ANDRADE, D. F. Time series and multilevel modeling for longitudinal item response theory data. 2018.
    [ Google | Google Scholar ]
  2. Vilca, F. Deletion measures for Linear Mixed Models using Skew-Normal/Independent Distributions. 2018.
    [ Google | Google Scholar ]
  3. Vilca, F. A robust multivariate Birnbaum-Saunders distribution: EM-estimation. 2018.
    [ Google | Google Scholar ]
  4. Vilca, F. Linear Mixed Models using Skew-Normal/Independent Distributions: Diagnostic analysis. 2018.
    [ Google | Google Scholar ]
  5. Vilca, F. A robust multivariate Birnbaum-Saunders distribution: EM-estimation. 2018.
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  6. Vilca, F. A Robust multivariate Birnbaum-Saunders regression models. 2018.
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  7. Pinheiro, Hildete; Maia, R. P.; Lima Neto, Eufrasio A. Discrete survival models to evaluate performance of students. 2018.
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  8. PINHEIRO, H. P.; Maia, R. P.; Lima Neto, Eufrasio A. A bivariate discrete survival mixed model to evaluate performance of students in Calculus I and Linear Algebra. 2018.
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  9. MATOS, L. A. Estimation and diagnostics in multivariate models for censored data. 2018.
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  10. TRUCIOS-MAZA, C. C.; Hotta, Luiz K.; PEREIRA, Pedro Luiz Valls On the robustness of the principal volatility components. 2018.
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  11. Garcia, N. L.; Rodrigues-Motta, M.; PETKOVA, E.; OGDEN, R. T.; TARPEY, T. Identification of early respondents based on EEG signals. 2018.
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  12. ZEVALLOS, Mauricio; TRUCÍOS, CARLOS; HOTTA, LUIZ KOODI; SANTOS, A. P. Covariance Prediction in Large Portfolio Allocation. 2018.
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  13. Garcia, Nancy L. Perfect Simulation for Spatial Processes. 2018.
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  14. Garcia, Nancy L. Mulheres palestrantes em congressos: por que tão poucas?. 2018.
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  15. FREGUGLIA, V.; Garcia, Nancy L. Modeling textile images with hidden Gibbs random fields. 2018.
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  16. MAIA, R. P. A importância da Estatística na Pesquisa Científica. 2018.
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  17. MAIA, R. P. ESTATÍSTICA APLICADA AO PROCESSO DE PESQUISA COM USO DO SOFTWARE ?R?. 2018.
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  18. MAIA, R.P.; Pinheiro, Hildete Prisco; LIMA NETO, E. A. A BIVARIATE DISCRETE SURVIVAL MIXED MODEL TO EVALUATE PERFORMANCE OF STUDENTS IN CALCULUS I AND LINEAR ALGEBRA. 2018.
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  19. MARQUES, R. N.; MAIA, E. F. S.; MAIA, R.P. INDICADORES DE DESEMPENHO DOS ALUNOS REINCIDENTES NAS DISCIPLINAS DE EXATAS DO CURSO SEMIPRESENCIAL DE LICENCIATURA EM CIÊNCIAS. 2018.
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  20. Kiihl, Samara F.; GARRIDO, M. J.; DOMINGO-RELLOSO, A.; BERMUDEZ, J.; TELLEZ-PLAZA, M. MLML2R: EFFICIENT SIMULTANEOUS MAXIMUM LIKELIHOOD ESTIMATION OF DNA METHYLATION AND HYDROXYMETHYLATION PROPORTIONS. 2018.
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  22. FERNÁNDEZ, M.; GARCÍA, JESÚS E.; GONZÁLEZ-LÓPEZ, V.A.; ROMANO, N. Bayesian sensitivity analysis for asymmetric copulas with cubic sections. 2018.
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  23. GARCÍA, JESÚS E.; GONZÁLEZ-LÓPEZ, V.A. Similarity between Strains of Zika from Tropical and Subtropical Regions. 2018.
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  24. González-López, V.A.; RODRIGUES, N.; ROMANO, N. Copula Model between Rates of Sexually Transmitted Diseases and Rates of Unemployment. 2018.
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  25. GONZÁLEZ-LÓPEZ, V.A.; RODRIGUES, N.; ROMANO, N. Upper Tail Dependence between Rates of Sexually Transmitted Diseases and Rates of Unemployment/Poverty. 2018.
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2017

  1. SANTOS, J. R. S.; AZEVEDO, C. L. N. A general Cholesky decomposition based modeling of longitudinal IRT data. 2017.
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  2. AZEVEDO, C. L. N.; ANDRADE, D. F. Time Series and Multilevel Modeling for Longitudinal Item Response Theory Data. 2017.
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  3. PINHEIRO, H. P.; Maia, R. P.; Barboza, Daniel Multinomial and discrete survival models to evaluate performance of students. 2017.
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  4. MATOS, L. A. Heavy-Tailed Longitudinal Linear Mixed Models for Multiple Censored Responses Data. 2017.
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  5. MATOS, L. A. Heavy-tailed longitudinal regression models for censored data: A likelihood based perspective. 2017.
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  6. Almeida, Daniel de; FUERTES, A.; Hotta, Luiz K. Equity Premium Prediction by Sparse Pooling of Parsimonious State-Dependent Models. 2017.
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  7. TRUCÍOS, C.; Hotta, Luiz K.; RUIZ, E. Robust bootstrap densities for dynamic conditional correlation. 2017.
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  8. Almeida, Daniel de; FUERTES, A.; Hotta, Luiz K. Forecast combination with state-dependent models: equity premium prediction. 2017.
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  9. Rodrigues-Motta, M.; Garcia, N. L.; BELAZ, K. R. A.; TATA, A.; FRANCA, M. R.; BINELLI, M.; CABERLIN, M. E. A Mixture of Bivariate Truncated Beta Distribution Applied to the Classification of Matrix Assisted Laser Desorption Ionization Mass Spectrometry (MALDI-MS) Data. 2017.
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  10. Rodrigues-Motta, M.; Claudia Akemi Koda; Verly Junior, E. Mixed models for positive responses augmented by zero. 2017.
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  11. Rodrigues-Motta, M. Added value from Classic to Bayesian approach. 2017.
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  12. Garcia, Nancy L. Why settle for MCMC samples when you can perfect simulate from the invariant distribution?. 2017.
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  13. MAIA, R. P. Modelos não-lineares mistos aplicados ao ajuste de curvas de produção de ovos de aves poedeiras.. 2017.
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  14. DIAS, R.; LUDWIG, G.; SAMPSON, P. A bit of splines, deformation, diffeomorphism, non-euclidean metrics toward spatial temporal processes. 2017.
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  15. TELLEZ-PLAZA, MARIA; Kiihl, Samara F.; MARTINEZ-GARRIDO, MARIA JOSE; DOMINGO-RELLOSO, ARCE; BERMUDEZ, JOSE The MLML2R package: maximum likelihood estimates of DNA methylation and hydroxymethylation using R. 2017.
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  16. Benaglia, T. Curso de Diseño y Análisis Experimental. 2017.
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  17. FERNANDEZ, M.; GARCÍA, JESÚS E.; GONZA'LEZ-LO'PEZ, V. A.; ROMANO, N. Bayesian Sensitivity Analysis for Asymmetric Copulas with Cubic Sections. 2017.
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  18. FERNÁNDEZ, M.; GARCÍA, JESÚS E.; GONZÁLEZ-LÓPEZ, V.A.; ROMANO, N.; TESSLER, J. Foreing exchange dependence through different copula models. 2017.
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  19. García, Jesús E.; González-López, V. A. Stochastic Distance between Burkitt lymphoma/leukemia Strains. 2017.
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  20. V. A. González-López A copula-based partition Markov procedure. 2017.
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  21. GONZÁLEZ-LÓPEZ, V.A. Consistent Estimation of Partition Markov Models. 2017.
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2016

  1. Ferreira, Eduardo V.; AZEVEDO, C. L. N. Modelos da Teoria de Resposta ao Item assimétricos de grupos múltiplos para respostas politômicas nominais e ordinais sob um enfoque bayesiano. 2016.
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  2. PINHEIRO, H. P.; Maia, R. P.; Lima Neto, Eufrasio A.; Motta, R.M. Modelling the proportion of failed courses and GPA scores for engineering major students. 2016.
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  3. PINHEIRO, H. P.; Maia, R. P.; Barboza, Daniel Análise do desempenho dos alunos da Unicamp em Cálculo I e Física I. 2016.
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  4. Rodrigues-Motta, M.; FORKMAN, J. Positive Responses Augmented by Zeros in Experiments with Incomplete Block Design. 2016.
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  5. ZEVALLOS, Mauricio Minimum Distance Estimation of Stochastic Conditional Duration Models. 2016.
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  6. Garcia, Nancy L. Analysis of aggregated functional data from mixed populations with Application to Energy Consumption. 2016.
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  7. MAIA, R.P.; Demétrio, CGB; Labouriau, R Variance component estimation on discrete time competing riskis analysis with masked causes. 2016.
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  8. MAIA, R.P.; Labouriau, R; Demétrio, CGB Discrete time competing risks analysis with masked causes. 2016.
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  9. MAIA, R.P.; Demétrio, CGB; LABOURIAU, R. A Discrete Mixed Competing Risk Model for Discrete Time with Masked Causes. 2016.
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  10. TELLEZ-PLAZA, M.; MARTINEZ-GARRIDO, MARIA JOSE; Kiihl, Samara F.; BERMUDEZ, JOSE; RENTERO-GARRIDO, P.; TANG, W. Y.; NAVAS-ACIEN, A. Estimation of consistent estimates of DNA methylation and hydroxymethylation from blood epidemiologic samples. 2016.
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  11. FERNANDEZ, M.; GONZA'LEZ-LO'PEZ, V. A.; HERGERT, B. S. Prediction through a Gumbel Barnett Copula. 2016.
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  12. FERNÁNDEZ, M.; GARCIA, J.; GONZA'LEZ-LO'PEZ, V. A.; ROMANO, N. Right Tail Increasing Dependence between Scores. 2016.
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  13. FERNÁNDEZ, M.; GARCÍA, JESÚS; GONZÁLEZ-LÓPEZ, V.A.; Viola, M. L. L. Full Interaction Partition Estimation in Stochastic Processes. 2016.
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2015

  1. Gomez, J. L. P.; AZEVEDO, C. L. N. Modelos da Teoria de Resposta ao Item Multidimensionais Assimétricos de Grupos Múltiplos Para Respostas Dicotômicas sob um Enfoque Bayesiano. 2015.
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  2. Chaves, N. L.; AZEVEDO, C. L. N. Um estudo sobre os aspectos inferenciais da distribuição Birnbaum-Saunders Skew-Normal sob a parametrização centrada. 2015.
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  3. Silva, A. R. S.; AZEVEDO, C. L. N.; Bazan, J. L. Modelo de Regressão Beta Retangular Aumentado em zeros e uns. 2015.
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  4. Lebensztayn, E. A large deviations principle for the Maki-Thompson rumour model. 2015.
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  5. Lebensztayn, E. Large deviations for rumour models. 2015.
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  6. VILCA, FILIDOR The Bivariate Sinh-Elliptical Distribution with Applications to Birnbaum-Saunders Distribution and Associated Regression and Measurement Error Models. 2015.
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  7. Pinheiro, Hildete P.; Maia, R. P.; PINHEIRO, A. S.; RODRIGUES-MOTTA, MARIANA; Franco, Gabriel Analysis of academic performance of students via quasi U-statistics and generalized linear mixed models. 2015.
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  8. PINHEIRO, H. P.; Maia, R. P.; PINHEIRO, A. S. Academic performance of students from entrance to graduation via quasi U-statistics. 2015.
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  9. CASCONE, M. H.; Hotta, Luiz K. Quasi-likelihood estimation of GARCH models with missing values. 2015.
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  10. de ALMEIDA, D.; Hotta, Luiz K.; RUIZ, E. MGARCH models: Tradeoff between feasibility and dynamic dependencies in volatilities and covariances. 2015.
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  11. ZEVALLOS, Mauricio; ABBARA, OMAR Assessing Stock Market Dependence and Contagion. 2015.
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  12. ZEVALLOS, Mauricio; ABBARA, OMAR Forecasting intraday VaR of an equity portfolio. 2015.
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  13. ZEVALLOS, Mauricio Minimum Distance Estimation of Stochastic Conditional Duration Models. 2015.
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  14. ZEVALLOS, Mauricio Estimation of the Autococorrelation Function of Long-Memory Processes. 2015.
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  15. Garcia, Nancy L.; Dias, Ronaldo; Schmidt, A.; SARAIVA, M. A.; LUDWIG, G. Análise estatística de dados de espectroscopia do infra-vermelho. 2015.
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  16. Garcia, Nancy L.; GALLO, S.; RODRIGUEZ, P.; JUNIOR, VALDIVINO VARGAS Rumor processes viewed as renewal processes. 2015.
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  17. Garcia, Nancy L. Análise de Dados Funcionais em Quimiometria. 2015.
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  18. Garcia, Nancy L. What is Perfect Simulation, and What Can it Do Perfectly for You. 2015.
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  19. Garcia, Nancy L. Quimiometria e Análise de Dados Funcionais. 2015.
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  20. Garcia, Nancy L. Why should we perfect simulate?. 2015.
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  21. Garcia, Nancy L. Why settle for MCMC samples when you can perfect simulate from the invariant distribution?. 2015.
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  22. LENZI, A.; Souza; DIAS, R.; GARCIA, NANCY L.; HECKMAN, NANCY Analysis of Aggregated Functional Data from Mixed Populations with Application to Energy Consumption. 2015.
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  23. García, J. E.; V. A. González-López; Viola, M. L. L. Copula-Based Analysis of Rhythm. 2015.
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  24. García, J. E.; V. A. González-López; Hirsh, I. Copula-Based Prediction of Economic Movements. 2015.
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  25. V. A. González-López A Copula-Based Partition Markov Procedure. 2015.
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  26. García, J. E.; V. A. González-López Dependency between clinical assessment and kinematic variables assessed with robotic device. 2015.
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  27. García, J. E.; V. A. González-López Markov Partition Models for Epstein Barr Virus. 2015.
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2014

  1. Nogarotto, D.; AZEVEDO, C.L.N. Bayesian inference in beta and inflated beta regression models. 2014.
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  2. Gomez, J. L. P.; AZEVEDO, C.L.N. Multidimensional Item Response Theory models with multivariate skew normal latent trait distributions under the centered parameterization. 2014.
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  3. AZEVEDO, C. L. N.; Vilca-Labra, F.; Lobos, C. Bayesian inference for Birnbaum-Saunders mixed models for censored responses. 2014.
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  4. AZEVEDO, C.L.N. Introdução à Teoria da Resposta ao Item. 2014.
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  5. AZEVEDO, C. L. N.; Fox, Jean-Paul; ANDRADE, D. F. Bayesian longitudinal item response modeling with restricted covariance pattern structures. 2014.
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  6. Pinheiro, Hildete P.; Maia, R. P.; PINHEIRO, A. S.; RODRIGUES-MOTTA, MARIANA; Franco, Gabriel Análise de Desempenho Acadêmico de estudantes da Unicamp. 2014.
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  7. Pinheiro, Hildete P.; PINHEIRO, A. S.; Kiihl, Samara F.; SEN, Pranab Kumar Comparison of groups of longitudinal sequences by quasi U-statistics. 2014.
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  8. Pinheiro, Hildete P.; RODRIGUES-MOTTA, MARIANA; Franco, Gabriel Modelling performance of students with bivariate generalized mixed models. 2014.
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  9. PINHEIRO, H. P.; Maia, R. P.; PINHEIRO, A. S.; Motta, R.M.; Franco, Gabriel Análise da performance acadêmica de estudantes via quase U-estatísticas e modelos lineares generalizados mistos. 2014.
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  10. Rodrigues-Motta, M.; SOTO, D. M. G.; Lachos, V. H.; VILCA, FILIDOR; BALTAR, V. T.; Verly Junior, E.; Fisberg, R.M.; Marchioni, Dirce M. Lobo A mixed model for positive responses augmented by zeros. 2014.
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  11. Rodrigues-Motta, M.; SOTO, D. M. G.; Lachos, V. H.; VILCA, FILIDOR; BALTAR, V. T.; Verly Junior, E.; Fisberg, Regina M.; Marchioni, Dirce M. Lobo A mixed-effect model for positive responses augmented by zeros. 2014.
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  12. ZEVALLOS, Mauricio; Carlos del Carpio; VILLARREAL, F. Estimación del Riesgo Bursátil Peruano: cuánto influyen los metales y el mercado internacional?. 2014.
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  13. ZEVALLOS, Mauricio Minimum Distance Estimation of High Frequency Transaction Data. 2014.
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  14. GALLO, SANDRO; Garcia, Nancy L.; JUNIOR, VALDIVINO VARGAS; RODRIGUEZ, P. Rumor spread viewed as renewal processes. 2014.
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  15. GALLO, SANDRO; Garcia, Nancy L.; JUNIOR, VALDIVINO VARGAS; RODRIGUEZ, P. Rumor processes viewed as renewal processes. 2014.
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  16. Dias, Ronaldo; Garcia, Nancy L.; Schmidt, A.; LUDWIG, G.; SARAIVA, M. A. Statistical analysis of NIR spectroscopy data. 2014.
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  17. Garcia, Nancy L.; Schmidt, A.; SARAIVA, M. A.; Dias, Ronaldo Chemometrics and Funcional Data Analysis. 2014.
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  18. DIAS, R. Modeling Aggregate Funcional Data. 2014.
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  19. DIAS, R.; Souza; LENZI, A.; HECKMAN, N.; GARCIA, N. L. Analysis of Aggregated Functional Data from Mixed Populations with Application to Energy Consumption. 2014.
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  20. SIGRIST, T. M.; KIIHL, S. F. CETvis. 2014.
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  21. Kiihl, Samara F.; Irizarry, Rafael Statistical methods for preprocessing, integration, and quality assessment of synthetic lethality data. 2014.
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  22. Kiihl, Samara F.; Irizarry, Rafael Statistical methods for preprocessing, integration, and quality assessment of synthetic lethality data. 2014.
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  23. Kiihl, Samara F.; Irizarry, Rafael PREPROCESSING AND QUALITY ASSESSMENT OF MICROARRAY-BASED SYNTHETIC LETHALITY DATA. 2014.
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  24. González-López, V. A. Open Problems (Theory of Copulas). 2014.
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  25. Bianchi, M. C.; Fernandes-Svartman, F. R.; FERNANDEZ, M.; González-López, V. A. Confidence Bands in Kendall Plots. 2014.
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  26. García, J. E.; González-López, V. A. Detecting regime changes in Markov models. 2014.
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  27. García, J. E.; González-López, V. A. An independence test based on random permutations. 2014.
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  28. González-López, V. A. A estatística é pop. 2014.
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  29. García, J. E.; González-López, V. A. Minimal Markov Models. 2014.
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2013

  1. Ferreira, Eduardo V.; AZEVEDO, C. L. N. Estimação paramétrica no modelo de crédito parcial generalizado da Teoria da Resposta ao Item. 2013.
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  2. SANTOS, J. R. S.; AZEVEDO, C. L. N. Um modelo de resposta ao item para grupos múltiplos com distribuições normais assimétricas centradas. 2013.
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  3. Vilca-Labra, F.; AZEVEDO, C.L.N.; Balakrishnan, N. Bayesian inference in Birnbaum-Saunders Nonlinear Regression Models based on Scale-Mixture of Normals. 2013.
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  4. AZEVEDO, C.L.N.; SANTOS, J. R. S. On the using of distances to measure goodness of fit in Item Response Theory models. 2013.
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  5. Flores, S.; Bazan, J.; AZEVEDO, C.L.N. A new testlet model. 2013.
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  6. PINHEIRO, H. P.; PINHEIRO, A. S.; SEN, Pranab Kumar Comparação de grupos de sequências genômicas via quase U-estatísticas. 2013.
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  7. RIBEIRO, A. L. P.; Hotta, Luiz K. Estimation and testing of hypotheses in the heteroskedastic canonical model of contagion using instrumental variables. 2013.
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  8. Hotta, Luiz K.; Tsai, R. Generalization of the Mixture Model Using a Copula Function. 2013.
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  9. TRUCIOS-MAZA, C. C.; Hotta, Luiz K. Bootstrap prediction in DCC-GARCH multivariate volatility model with normal distribution. 2013.
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  10. CASCONE, M. H.; Hotta, Luiz K. Efeito dos outliers na estimação dos modelos de volatilidade estocástica. 2013.
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  11. Garcia, Nancy L. Perfect simulation for chains of infinite order. 2013.
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  12. DIAS, R.; GARCIA, N. L.; Schmidt, A. M. A Hierarchical Model for Aggregated Functional Data: Abayesian Approach. 2013.
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  13. DIAS, R.; GARCIA, N. L.; ALEXANDRA, A Hierarchical Model for Aggregated Functional Data. 2013.
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  14. GARCIA, J.; González-López, V. A.; Nelsen, R. A New Index to Measure Positive Dependence in Trivariate Distributions. 2013.
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  15. FERNANDEZ, M.; González-López, V. A. Bayesian approach for convex combination of two Gumbel-Barnett copulas. 2013.
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  16. García, J. E.; González-López, V. A. Detecting Regime Changes In Markov Models. 2013.
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  17. González-López, V. A. Aspectos Metodológicos na Análise Estatística em Saúde: a Visão Bayesiana. 2013.
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  18. FERNANDEZ, M.; González-López, V. A.; Rifo, L. L. R. A Note on Conjugate Distibutions for Likelihoods Given by Copulas. 2013.
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2012

  1. PINHEIRO, A. CTARMA Wavelet Functional Data Model. 2012.
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  2. Valk, Marcio; PINHEIRO, A. Classificação e Agrupamento de Séries Temporais Multivariadas via Quase U-Estatísticas. 2012.
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  3. PINHEIRO, Hildete Prisco; PINHEIRO, A.; SEN, Pranab Kumar Comparison of groups of dependent sequences by quasi U-statistics. 2012.
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  4. Borgatto, A.F.; Azevedo, C.L.N.; PINHEIRO, A.; Andrade, D.F. Comparação entre Métodos de Estimação das Habilidades na TRI em Provas com Diferentes Graus de Dificuldade. 2012.
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  5. PINHEIRO, A.; SEN, Pranab Kumar THE USE OF QUASI U-STATISTICS FOR EXCHANGEBLE RANDOM VECTORS. 2012.
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  6. Valk, Marcio; PINHEIRO, A. Clustering time series with time dependents outliers. 2012.
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  7. Kist, A.; PINHEIRO, A. Procedimento iterativo Cochrane-Orcutt em modelos FANOVA de efeitos fixos com erros dependentes. 2012.
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  8. AZEVEDO, C. L. N. CADEM: algoritmo EM condicional via dados aumentados para ajuste de modelos da TRI. 2012.
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  9. Azevedo, Caio L. N.; Migon, H. S. Bayesian Inference In A Item Response Theory Model With A Generalized Student t Link Function. 2012.
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  10. SANTOS, J. R. S.; Azevedo, Caio L.N. Um Modelo de Resposta ao Item para Grupos Múltiplos com Distribuições Normais Assimétricas Centralizadas. 2012.
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  11. Ferreira, Eduardo V.; Azevedo, Caio L.N. Estimação Paramétrica no Modelo de Crédito Parcial Generalizado da Teoria da Resposta ao Item. 2012.
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  12. Pereira, S. R. S.; AZEVEDO, C. L. N. Contribuições ao estudo do Modelo de Resposta Nominal. 2012.
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  13. García, Jesús E.; González-López, V. A.; LANFREDI VIOLA, Marcio Luis Model selection for multivariate Markov chains. 2012.
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  14. Tsai, R.; Hotta, Luiz K. Modelo de Misturas de Distribuições Utilizando Cópulas. 2012.
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  15. Tsai, R.; Hotta, Luiz K. A generalization of the mixture model using a copula function. 2012.
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  16. RODRIGUES-Motta, M.; Lachos, V. H.; Labra, F; Verly Junior, E.; Fisberg, R.M.; MARCHIONI, D. D. Modeling food intake using the Zero-Inflated Gamma Regression Model. 2012.
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  17. RODRIGUES-Motta, M.; Labra, F; LEIVA, V. Generalized Logarithm and Normal Scale Mixture Distributions with an Application in Genetics. 2012.
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  18. Rodrigues-Motta, M.; VILCA, FILIDOR; LEIVA, V. Generalized Logarithm and Normal Scale Mixture Distributions with an Application in Genetics. 2012.
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  19. Garcia, Nancy L.; DIAS, R.; ZAMBOM, A. Z. How to find the best path for an autonomous vehicle. 2012.
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  20. Garcia, Nancy L. Abertura das atividades de PG em Matemática e Estatística. 2012.
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  21. Dias, Ronaldo; Garcia, Nancy L.; LUDWIG, G. Análise de dados de espectroscopia do infra-vermelho. 2012.
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  22. Garcia, Nancy L. Perfect simulation for chains and processes of infinite order. 2012.
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  23. DIAS, R.; GARCIA, N. L.; Schmidt, A. M. Hierarchical Model For Aggregated Functional Data. 2012.
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  25. García, J. E.; González-López, V. A.; Viola, M. L. L. Robust model selection for stochastic processes. 2012.
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  26. García, J. E.; González-López, V. A.; Nelsen, R. A New Index to Measure Dependence. 2012.
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  27. García, J. E.; González-López, V. A. An independence test based on random permutations. 2012.
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  28. García, J. E.; González-López, V. A.; Viola, M. L. L. Markovian Model Selection for Contaminated Stochastic Processes. 2012.
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2011

  1. PINHEIRO, A.; SEN, Pranab Kumar Quasi U-statistics and Functional Data Analysis. 2011.
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  2. Pinheiro, Aluísio; SEN, Pranab Kumar; PINHEIRO, Hildete Prisco A class of asymptotically normal degenerate quasi U -statitiscs. 2011.
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  5. Ferreira, Eduardo V.; Azevedo, Caio L. N. Um estudo inferencial sobre o modelo de crédito parcial generalizado da teoria da resposta ao item com aplicação aos dados do vestibular da UNICAMP. 2011.
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  6. Ferreira, Eduardo V.; Azevedo, Caio L. N. Um estudo inferencial sobre o modelo de crédito parcial generalizado teoria da resposta ao item. 2011.
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  7. SCHWELLER, L. S.; Azevedo, Caio L.N. Um estudo sobre os aspectos inferenciais bayesianos da distribuição lognormal sob uma nova parametrização. 2011.
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  8. SCHWELLER, L. S.; Azevedo, Caio L.N. Um estudo sobre os aspectos probabilísticos e inferenciais frequentistas da distribuição lognormal sob uma nova parametrização. 2011.
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  9. Azevedo, Caio L. N.; Migon, H. S. Inferência Bayesiana em um modelo da TRI com função de ligação t de Student Generalizada. 2011.
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  10. Pereira, S. R. S.; Azevedo, Caio L. N. Comparação entre o modelo de resposta nominal e os modelos de respostas dicotômicas de dois parâmetros (ML2P) e três parâmetros (ML3P). 2011.
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  11. Pereira, S. R. S.; Azevedo, Caio L.N. Análise dos Parâmetros dos Itens do Modelo de Resposta Nominal. 2011.
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  12. Pose, R. A.; FREIRE, M. V.; Azevedo, Caio L. N.; SILVA, F. D.; Oliveira, E. A.; Vieira, J. E.; COSTA JR, J. O. A.; CARMONA, M. J.; SIMOES, C. M. Avaliação do risco de complicações perioperatórias do índice (POSSUM) em pacientes submetidos a cirurgia de cabeça e pescoço: Aplicação da Teoria de Resposta ao Item (TRI). 2011.
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  13. Veloso, M. V. S.; Azevedo, Caio L. N.; Bueno, J. S. S. Seleção de variáveis por busca estocástica: uma aplicação ao modelo de Birnbaum da Teoria de Resposta ao Item. 2011.
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  14. SANTOS, J. R. S.; Azevedo, Caio L. N. Um Modelo de Resposta ao Item para Grupos Múltiplos com Distribuições Normais Assimétricas Centralizadas. 2011.
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  15. GARCIA, J. E.; González-López, V. A. Minimal Partition Models. 2011.
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  16. GARCIA, J. E.; Galves, A.; GALVES C; GARCIA, N.; Leonardi, Florencia Context tree selection and linguistic rhythm retrieval from written texts. 2011.
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  17. García, Jesús E.; González-López, V. A. Minimal Markov Models. 2011.
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  18. García, Jesús E.; González-López, V. A. A non-parametric test of independence. 2011.
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  19. Tsai, R.; Hotta, Luiz K. Polyhazard Models with Dependent Causes. 2011.
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  20. Tsai, R.; Hotta, Luiz K. Polyhazard Models with Dependent Causes. 2011.
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  21. ZEVALLOS, Mauricio; HOTTA, L.K. Influential Observations in GARCH models. 2011.
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  22. Garcia, Nancy L. Cadeias de Markov em dados linguísticos. 2011.
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  23. Garcia, Nancy L.; GALLO, S. Chains of infinite order - construction and perfect simulation. 2011.
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  24. Lopes, H.; DIAS, R. Bayesian Mixture of parametric and nonparametric density estimation: A misspecification Problem. 2011.
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  25. García, J. E.; González-López, V. A. Speech signal processes discriminate between rhythmic classes. 2011.
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  26. García, J. E.; González-López, V. A. Minimal Markov Models. 2011.
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  27. González-López, V. A.; García, J. E.; Nelsen, R. A New Index to Measure Positive Dependence in Trivariate Distributions. 2011.
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  28. García, J. E.; González-López, V. A. Minimal Markov Models. 2011.
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  29. González-López, V. A. A New Index to Measure Positive Dependence. 2011.
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  30. García, J. E.; González-López, V. A.; Nelsen, R. A New Index to Measure Dependence. 2011.
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2010

  1. PINHEIRO, A.; SEN, Pranab Kumar Functional Data Analysis Via Quasi U- Statistics. 2010.
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  2. Kist, A.; PINHEIRO, A. O uso de ondaletas para ANOVA funcional. 2010.
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  3. PINHEIRO, A.; PINHEIRO, Hildete Prisco; SEN, Pranab Kumar The use of quasi U-statistics for undergraduate performance assessment. 2010.
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  4. Cuyabano, B.; PINHEIRO, Hildete Prisco; PINHEIRO, A. Generalized Linear Models Applied to DNA Sequences. 2010.
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  5. motta, mariana rodrigues; PINHEIRO, A. On the categorization of a beta-uniform distribution and its application to the gene expression problem. 2010.
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  6. Valk, Marcio; PINHEIRO, A. Agrupamentos de Series Temporais Via Quase U-Estatisticas. 2010.
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  7. PINHEIRO, A.; SEN, Pranab Kumar; PINHEIRO, Hildete Prisco A Class of Asymptotically Normal Degenerate Quasi U-Statistics. 2010.
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  8. PINHEIRO, A.; SEN, Pranab Kumar; PINHEIRO, Hildete Prisco Uma Classe de Quase U-estatísticas Degeneradas Assintoticamente Normais. 2010.
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  9. AZEVEDO, C. L. N.; Bolfarine, H.; ANDRADE, D. F. Bayesian inference for a skew-normal IRT model under the centred parameterization. 2010.
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  10. Azevedo, Caio L. N.; Lopes, H.; ANDRADE, D. F. Dynamic Item Response Models. 2010.
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  11. AZEVEDO, C. L. N.; Bolfarine, H.; ANDRADE, D. F. An IRT probit model with skew-normal latent traits distribution: An application to the measurement of depression levels. 2010.
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  12. VICINI, L.; Hotta, Luiz K.; ACHCAR, J. A. Processos de Poisson não-homogêneo, estimando o número excessos de ozônio, para um limiar, na Cidade do México. 2010.
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  13. ALONSO, Jonas Bodini; Hotta, Luiz K.; ACHCAR, J. A. Análise de dados de contagem correlacionados através da distribuição Poisson bivariada. 2010.
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  14. SAKAMOTO, C. F.; Hotta, Luiz K. Ajuste de Modelos Pair-Cópula a Índices de Mercado. 2010.
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  15. DINIZ, M. A.; Hotta, Luiz K.; ACHCAR, J. A. Estimação de Modelos SEIR Estocásticos com Dados Incompletos.. 2010.
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  16. DINIZ, M. A.; Hotta, Luiz K. Modelos Compartimentais Determinísticos e Estocásticos: Modelagem de Epidemias. 2010.
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  17. Tsai, R.; Hotta, Luiz K. Competing Risks Models with Unobservable Dependent Failure Causes. 2010.
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  18. RODRIGUES-Motta, M.; PINHEIRO, A. S. Categorizing a beta-uniform distribution. 2010.
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  19. Motta, M. R.; PINHEIRO, A. S. On the categorization of a beta-uniform distribution and its application to the gene expression problem. 2010.
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  20. RODRIGUES-Motta, M.; Labra, F; LEIVA, V. On the glog-SMN distribution and its aplication to a gene expression problem. 2010.
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  21. ZEVALLOS, Mauricio; Carlos del Carpio Metal Prices, Stock Returns and Stock Market Volatility. 2010.
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  22. MAIA, A. H. E. N.; ZEVALLOS, Mauricio; POTGIETER, A.; MEINKE, H Statistical Evidence of unusual Southern Oscillation Index patterns suggests possible climate change impacts on ENSO behavior. 2010.
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  23. Garcia, Nancy L. Best path for automatous vehicles. 2010.
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  24. DIAS, R.; GARCIA, N. L.; Zambom, A. Z. Monte Carlo algorithm for trajectory optimization based on Markovian readings. 2010.
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  25. DIAS, R. A Bayesian Approach to Multivariate H-spline Nonparametric Regression. 2010.
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  26. García, J. E.; González-López, V. A. An independence test based on random permutations. A remarkable performance in samples from mixture models. 2010.
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  27. González-López, V. A.; Rifo, L. L. R. Full Bayesian analysis for asymmetric logistic copulas. 2010.
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  28. González-López, V. A.; Singer, J. M.; Tanaka, N. I.; Lima, A. C. P. A Bayesian Test for the Intraclass Correlation Coefficient. 2010.
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  29. García, J. E.; González-López, V. A. BIC Criterion for Minimal Markov Models. 2010.
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2009

  1. AZEVEDO, C. L. N.; Bolfarine, H.; ANDRADE, D. F. Análise bayesiana de um modelo da TRI de 2 parâmetros com distribuição normal assimétrica latente. 2009.
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  2. AZEVEDO, C. L. N.; Bolfarine, H.; ANDRADE, D. F. Modelo longitudinal AR(1) homocedástico via séries temporais da TRI,. 2009.
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  3. AZEVEDO, C. L. N.; Lopes, H.; ANDRADE, D. F. Modelos Dinâmicos na Teoria da Resposta ao Item. 2009.
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  4. AZEVEDO, C. L. N.; Bolfarine, H.; ANDRADE, D. F. Modelo probito TRI assimétrico latente bayesiano com aplicação na modelagem de níveis de depressão. 2009.
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  5. AZEVEDO, C. L. N.; Bolfarine, H.; ANDRADE, D. F. Bayesian Inference For a Skew-Normal IRT Model Under The Centred Parametrization. 2009.
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  6. Pereira, S. R. S.; AZEVEDO, C. L. N. Comparação Entre a Teoria Clássica de Medida e a Teoria de Resposta ao Item: Um Estudo de Caso. 2009.
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  7. Pose, R. A.; AZEVEDO, C. L. N.; ANDRADE, D. F.; Daud-Gallotti, R.; Arlindo, M.; Martins, M. A.; Scalabrini-Neto, A.; Velasco, I. T.; Tibério, I. C. Avaliar Competências no Programa de Segurança do Paciente Durante o Internato da Faculdade de Medicina da USP Através da Metodologia OSCE (Organized Structure Clinical Examination) com Pacientes Simulados: Estudo Piloto Utilizando a TRI (Teoria da Resposta ao Item). 2009.
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  8. Pose, R. A.; AZEVEDO, C. L. N.; ANDRADE, D. F.; Rios, I. C.; Vieira, J. E.; Martins, M. A. O Programa de Avaliação Curricular das Disciplinas de Humanidades Médicas do Curso de Medicina da Faculdade de Medicina da USP: Início de um Estudo com a TRI (Teoria da Resposta ao Item). 2009.
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  9. Pose, R. A.; AZEVEDO, C. L. N.; ANDRADE, D. F.; Sandeville, M. L.; Vieira, J. E.; Martins, M. A. Teoria da Resposta ao Item: Aplicação de Análise de Itens nos Testes do Progresso na Faculdade de Medicina da USP: Projeto Piloto. 2009.
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  10. Pose, R. A.; AZEVEDO, C. L. N.; ANDRADE, D. F.; Vieira, J. E.; Martins, M. A. Utilização da Teoria da Resposta ao Item na análise de micronúcleos da Tradescantia pallida:Caracterização do Risco Ambiental como Ferramenta de Desenvolvimento de Projetos em Educação Ambiental.. 2009.
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  11. AZEVEDO, C. L. N.; FOX, J.; ANDRADE, D. F. Estimação e Métodos de Diagnóstico Bayesianos via Amostrador de Gibbs em Modelos Longitudinais da TRI sob uma Abordagem. 2009.
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  12. BUSATO, Erick Andrade; Hotta, Luiz K. Função de Acoplamento t-Student Assimétrica: Modelagem de Dependência Assimétrica. 2009.
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  13. RIBEIRO, A. L. P.; Hotta, Luiz K. Análise de contágio sob o enfoque de variáveis instrumentais. 2009.
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  14. Tsai, R.; Hotta, Luiz K. Modelos de riscos múltiplos com dependência. 2009.
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  15. BUSATO, Erick Andrade; Hotta, Luiz K. Skewed Student's t Copula: Modeling of Asymmetric Dependence.. 2009.
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  16. Laurini, Márcio Poletti; Hotta, Luiz K. Generalized latent factor models for yield curves in multiple markets. 2009.
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  17. RODRIGUES-Motta, M. Modelos com efeitos mistos para dados de Poisson com o zero inflacionado e com superdispersão. 2009.
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  18. ZEVALLOS, Mauricio; PALMA, Wilfredo Minimum Distance Estimation of Long-range Dependent Processes. 2009.
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  19. ZEVALLOS, Mauricio; HOTTA, L.K. Slope Influential Diagnostics in Conditional Heteroscedastic Time Series Models. 2009.
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  20. GARCIA, N. L.; DIAS, R.; Martarelli, A.; Schmidt, A. Aggregated functional data. Applications to electric load monitoring. 2009.
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  21. GARCIA, N. L.; GALVES, A.; Prieur, C. Monge-Kantorovich problem for chains with infinite memory. 2009.
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  22. DIAS, R.; GARCIA, N. L.; ZAMBOM, A. Z. Como encontrar o melhor caminho para um veiculo autonomo - caso markoviano. 2009.
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  23. DIAS, R.; GARCIA, N. L.; Zambom, A. Z. A penalized nonparametric method for nonlinear constrained optimization based on. 2009.
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  24. González-López, V. A.; García, J. E. A family of exact nonparametric independence tests for small samples. 2009.
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  25. Fernandes-Svartman, F. R.; Abaurre, M. B; González-López, V. A.; Bianchi, M. C. Secondary stress, intensity and F0 in Brazilian Portuguese. 2009.
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  26. García, J. E.; González-López, V. A. A nonparametric independence test using random permutations. 2009.
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  27. García, J. E.; González-López, V. A. A nonparametric independence test using random permutations. 2009.
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2008

  1. TAGLIANETTI, T. B.; PINHEIRO, Hildete Prisco; PINHEIRO, A. A Homogeneity Test Among Groups for Microsatelite Data. 2008.
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  2. SEN, Pranab Kumar; PINHEIRO, A.; PINHEIRO, Hildete Prisco A Class of Asymptotically Normal Degenerate Quasi U-Statistics. 2008.
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  3. PINHEIRO, A. Quasi U-Statistics: Natural Representation of Diversity Measures. 2008.
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  4. BORDIN, Tatiana Buratto; PINHEIRO, H. P.; PINHEIRO, Aluísio A homogeneity test among groups for microsatellite data. 2008.
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  5. Laurini, Márcio Poletti; Hotta, Luiz K. Bayesian extensions to Diebold-Li term structure model. 2008.
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  6. Laurini, Márcio Poletti; Hotta, Luiz K. Bayesian extensions to Diebold-Li term structure model. 2008.
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  7. ZEVALLOS, Mauricio; Bruno Reis dos Santos Local Influence in ARFIMA Models. 2008.
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  8. ZEVALLOS, Mauricio; PALMA, Wilfredo Minimum Distance Estimation of Long-range Dependent Processes. 2008.
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  9. ZEVALLOS, Mauricio Quantile Estimation for Dependent Data: Applications to Finance. 2008.
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  10. González-López, V. A.; García, J. E. An exact non parametric independence test. 2008.
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Arquivo gerado a partir dos currículos Lattes dos pesquisadores. Data do processamento: 02/06/2025 09:35:38
Os resultados estão sujeitos a falhas devido a eventuais inconsistências no preenchimento dos currículos Lattes pelos pesquisadores. Caso note algum problema, por favor, contacte o responsável por esta página: informatica@ime.unicamp.br.