Programa de Pós-Graduação em Estatística

Produção Científica do Período de 2008 a 2025

Demais tipos de produção bibliográfica

Número total de items: 52

2024

  1. JUSTUS, V. L.; RODRIGUES, V. B.; SOUSA, A. R. S. Bootstrap confidence intervals: A comparative simulation study. 2024.
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  2. SOUSA, A. R. S. A class of priors to perform asymmetric Bayesian wavelet shrinkage. 2024.
    [ Google | Google Scholar ]

2023

  1. BARROS, M. F. S.; STEFANO FILHO, C. A.; MENEZES, L. T.; ARAUJO-MOREIRA, F. M.; TREVELIN, L. C.; MAIA, R. P.; CASTELLANO, G.; RADEL, R. Psycho-Physio-Neurological Correlates of Qualitative Attention, Emotion and Flow Experiences in a Close-to-Real-Life Extreme Sports Situation: Low- aAnd High-Altitude Slackline Walking. 2023.
    [ Google | Google Scholar ]

2022

  1. Pereira, V.; Lebensztayn, E. Tópicos de Processos Estocásticos. 2022.
    [ Google | Google Scholar ]
  2. Garcia, N. L.; Rodrigues-Motta, M.; MIGON, H.; PETKOVA, E.; TARPEY, T.; OGDEN, R. T.; GIORDANO, J. O.; PEREZ, M. M. Unsupervised Bayesian classification for models with scalar and functional covariates. 2022.
    [ Google | Google Scholar ]
  3. FREGUGLIA, V.; Garcia, Nancy L. Sparse Interaction Neighborhood Selection for Markov Random Fields via Reversible Jump and Pseudoposteriors. 2022.
    [ Google | Google Scholar ]

2021

  1. PELCK, J. S.; Maia, R. P.; PINHEIRO, H. P.; Laboriau, R. A Multivariate Methodology for Analysing Students' Performance Using Register Data. 2021.
    [ Google | Google Scholar ]
  2. Singer, J.M.; Nobre, J.S.; ROCHA, F. M. M. Análise de Dados Longitudinais (versão parcial preliminar). 2021.
    [ Google | Google Scholar ]
  3. SOUSA, ALEX RODRIGO DOS S.; Garcia, Nancy L. Wavelet Shrinkage in Nonparametric Regression Models with Positive Noise. 2021.
    [ Google | Google Scholar ]
  4. FRANCO, G.; Souza, C. P.; Garcia, Nancy L. Aggregated functional data model applied on clustering and disaggregation of UK electrical load profiles. 2021.
    [ Google | Google Scholar ]
  5. FRANCO, G.; Souza, C. P.; Garcia, Nancy L. Aggregated functional data model applied on clustering and disaggregation of UK electrical load profiles. 2021.
    [ Google | Google Scholar ]

2020

  1. MEDEIROS, E.; LIRA, J.; SILVA, R.; AZEVEDO, C. L. N. Visualizing and Understanding Large-Scale Assessments in Mathematics through Dimensionality Reduction. 2020.
    [ Google | Google Scholar ]
  2. FREGUGLIA, V.; Garcia, Nancy L. Inference tools for Markov Random Fields on lattices: The R package mrf2d. 2020.
    [ Google | Google Scholar ]

2019

  1. TRUCÍOS, C.; Mazzeu, J. H. G.; Hallin, M.; Hotta, Luiz K.; Valls Pereira, Pedro L.; ZEVALLOS, M. Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: A General Dynamic Factor Approach. 2019.
    [ Google | Google Scholar ]
  2. CERQUEIRA, A.; GARCIA, N. L. Graphical Construction of Spatial Gibbs Random Graphs. 2019.
    [ Google | Google Scholar ]
  3. ZAMBOM, A. Z.; KIM, S.; Garcia, Nancy L. Variable Length Markov Chain with Exogenous Covariates. 2019.
    [ Google | Google Scholar ]
  4. SOUSA, ALEX RODRIGO DOS S.; Garcia, Nancy L.; VIDAKOVIC, BRANI Bayesian Wavelet Shrinkage with Beta Priors. 2019.
    [ Google | Google Scholar ]

2018

  1. Chaves, N. L.; AZEVEDO, C. L. N.; Vilca-Labra, F.; Nobre, J. S. A new Birnbaum-Saunders model based on the skew-normal distribution under the centred parameterization. 2018.
    [ Google | Google Scholar ]
  2. Chaves, N. L.; AZEVEDO, C. L. N.; Vilca-Labra, F.; Nobre, J. S. A log Birnbaum-Saunders regression model based on the skew-normal distribution under the centred parameterization. 2018.
    [ Google | Google Scholar ]
  3. PINHEIRO, H. P.; SEN, Pranab Kumar; PINHEIRO, A. S.; Kiihl, Samara F. A nonparametric approach to assess undergraduate performance. 2018.
    [ Google | Google Scholar ]

2017

  1. SANTOS, J. R. S.; AZEVEDO, C. L. N. Bayesian General Cholesky Decomposition Based Modeling of Longitudinal Multiple-Group IRT Data with Skewed Latent Distributions and Growth Curves. 2017.
    [ Google | Google Scholar ]
  2. SANTOS, J. R. S.; AZEVEDO, C. L. N. A Copula Based Modeling for Longitudinal IRT Data with Skewed Latent Distributions. 2017.
    [ Google | Google Scholar ]
  3. Chaves, N. L.; AZEVEDO, C. L. N.; Vilca-Labra, F.; Nobre, J. S. Bayesian Inference for a Birnbaum-Saunders Regression Model Based on the Centered Skew Normal Distribution. 2017.
    [ Google | Google Scholar ]
  4. Silva, A. R. S.; AZEVEDO, C. L. N.; Bazan, J. L.; Nobre, J. S. Likelihood-based Inference for Zero-or-one Augmented Rectangular Beta Regression Models. 2017.
    [ Google | Google Scholar ]
  5. Gomez, J. L. P.; AZEVEDO, C. L. N.; Lachos, V. H. Multidimensional Multiple Group IRT Models with Skew Normal Latent Trait Distributions. 2017.
    [ Google | Google Scholar ]
  6. Silva, A. R. S.; AZEVEDO, C. L. N.; Bazan, J. L.; Nobre, J. S. Bayesian Inference for Zero-and/or-one Augmented Rectangular Beta Regression Models. 2017.
    [ Google | Google Scholar ]
  7. SANTOS, J. R. S.; AZEVEDO, C. L. N. A General Cholesky Decomposition Based Modeling of Longitudinal IRT Data. 2017.
    [ Google | Google Scholar ]
  8. PINHEIRO, H. P.; M, Rafael; Lima Neto, E. A.; Rodrigues-Motta, M. Zero-one Augmented Beta and Zero Inflated Discrete Models with Heterogeneous Dispersion: An Application to Students Academic Performance. 2017.
    [ Google | Google Scholar ]

2016

  1. Nogarotto, D.; AZEVEDO, C. L. N.; Bazan, J. L. Bayesian estimation, residual analysis and prior sensitivity study for zero-one augmented beta regression model with an application to psychometric data. 2016.
    [ Google | Google Scholar ]
  2. PINHEIRO, H. P.; M, Rafael; Lima Neto, E. A.; Rodrigues-Motta, M. Modelling the proportion of failed courses and GPA scores for engineering major students. 2016.
    [ Google | Google Scholar ]

2015

  1. Pinheiro, Hildete P.; PINHEIRO, Aluísio; SEN, Pranab Kumar; Kiihl, Samara F. Comparison of groups of longitudinal sequences by quasi U-statistics. 2015.
    [ Google | Google Scholar ]
  2. Pinheiro, Hildete P.; RODRIGUES-MOTTA, MARIANA; Franco, Gabriel Modelling performance of students with generalized linear mixed models. 2015.
    [ Google | Google Scholar ]
  3. Almeida, Daniel de; Hotta, Luiz; RUIZ, E. MGARCH models: tradeoff between feasibility and flexibility. 2015.
    [ Google | Google Scholar ]
  4. TRUCÍOS, C.; Hotta, Luiz K.; RUIZ, E. Robust bootstrap forecast densities for GARCH models: returns, volatilities and value-at-risk. 2015.
    [ Google | Google Scholar ]

2014

  1. Lenzi, A.; Souza, C. P.; Dias, Ronaldo; Garcia, Nancy L.; HECKMAN, N. Analysis of Aggregated Functional Data from Mixed Populations with Application to Energy Consumption. 2014.
    [ Google | Google Scholar ]

2013

  1. COSTA, D.R.; Lachos, V. H.; Bazan, J. L.; AZEVEDO, C. L. N. Estimation Methods for Multivariate Tobit Confirmatory Factor Analysis. 2013.
    [ Google | Google Scholar ]
  2. Cuyabano, B.; PINHEIRO, H.P.; PINHEIRO, Aluísio Models Applied to DNA Sequences with Multinomial Correlated Responses. 2013.
    [ Google | Google Scholar ]
  3. Garcia, Nancy L.; Moreira, L. Stochastically Perturbed Chains of Variable Memory. 2013.
    [ Google | Google Scholar ]
  4. Dias, Ronaldo; Garcia, Nancy L.; LUDWIG, G.; SARAIVA, M. A. Aggregated functional data model for Near-Infrared Spectroscopy calibration and prediction. 2013.
    [ Google | Google Scholar ]
  5. Lachos, Victor. H.; CABRAL, C. R. B.; Garay, A.W. Modelos Nolineares Assimetricos. 2013.
    [ Google | Google Scholar ]
  6. Garay, Aldo M.; Lachos, Victor. H. Análise de dados censurados sob distribuições simétricas com aplicações no R. 2013.
    [ Google | Google Scholar ]

2012

  1. AZEVEDO, C. L. N.; Bolfarine, H.; ANDRADE, D. F. A note on identification and metric issues for skew IRT models. 2012.
    [ Google | Google Scholar ]
  2. AZEVEDO, C. L. N.; Migon, H. S. An IRT model with a generalized student t-link function. 2012.
    [ Google | Google Scholar ]
  3. SANTOS, J. R. S.; AZEVEDO, C. L. N.; BOLFARINE, H. A multiple group Item Response Theory model with centred skew normal latent trait distributions under a Bayesian framework. 2012.
    [ Google | Google Scholar ]
  4. AZEVEDO, C. L. N.; Fox, Jean-Paul; ANDRADE, D. F. Bayesian general multivariate latent variable modeling of longitudinal item response data. 2012.
    [ Google | Google Scholar ]

2011

  1. GALLO, S.; Garcia, Nancy L. A general context-tree-based approach to perfect simulation for chains of infinite order. 2011.
    [ Google | Google Scholar ]

2010

  1. GALLO, S.; GARCIA, N. L. Perfect simulation for stochastic chains of infinite memory: relaxing the continuity assumption. 2010.
    [ Google | Google Scholar ]
  2. GALVES, A.; GARCIA, N. L.; LOCHERBACH, E. Perfect simulation and finitary coding for multicolor systems with interactions of infinite range. 2010.
    [ Google | Google Scholar ]
  3. GALVES, A.; GARCIA, N. L.; Prieur, C. Perfect simulation of a coupling achieving the $\bar{d}$-distance between ordered pairs of binary chains of infinite order. 2010.
    [ Google | Google Scholar ]

2009

  1. AZEVEDO, C. L. N.; Gamerman, D. Introdução à Teoria da Resposta ao Item. 2009.
    [ Google | Google Scholar ]
  2. KIIHL, Samara Flamini; PINHEIRO, H. P.; PINHEIRO, Aluísio; REIS, Sérgio Furtado dos Alternative method for Tajma's test of selective neutrality. 2009.
    [ Google | Google Scholar ]
  3. GALVES, A.; GALVES, C.; GARCIA, N. L.; LEONARDI, F. Context tree selection and linguistic rhythm retrieval from written texts. 2009.
    [ Google | Google Scholar ]
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.