Programa de Pós-Graduação em Estatística

Produção Científica do Período de 2012 a 2025

Demais tipos de produção bibliográfica

Número total de items: 96

2025

  1. Pevide, P. M.; Lebensztayn, E. Processos de renovação e aplicações. 2025.
    [ Google | Google Scholar ]
  2. MATTA, D. H.; MOTTA, M. R.; Garcia, Nancy L.; HEINEMANN, A. B. A Bayesian Spatial-Temporal Functional Model for Data with Block Structure and Repeated Measures. 2025.
    [ Google | Google Scholar ]
  3. SOUSA, A. R. S.; ZEVALLOS, M. A note on wavelet shrinkage in nonparametric regression models with ARFIMA errors. 2025.
    [ Google | Google Scholar ]
  4. BARRIOS, F. A. C.; SOUSA, A. R. S. Bayesian wavelet shrinkage for low SNR data based on the Epanechnikov kernel. 2025.
    [ Google | Google Scholar ]
  5. FELIX, C. H. T. N.; GARCIA, N. L.; SOUSA, A. R. S. A Fuzzy Approach for Randomized Confidence Intervals. 2025.
    [ Google | Google Scholar ]
  6. BEN-ARI, Iddo; LEBENSZTAYN, Élcio; SANTOS, L. S. On quasi-stationary distributions for stochastic rumor models. 2025.
    [ Google | Google Scholar ]
  7. SANTOS, D. S.; PINHO, L. G. B. Modelling Asset Price Dynamics with Investor Inertia: Diffusion with Advection and Fourth-Order Extension. 2025.
    [ Google | Google Scholar ]
  8. BARRIOS, F. A. C. Comparação de desempenho de estimadores de coeficientes de ondaletas em dados com baixa razão sinal-ruído via simulações Monte Carlo. 2025.
    [ Google | Google Scholar ]
  9. REINA, J. M. Estimador bayesiano de coeficientes de ondaletas sob priori cosseno elevado. 2025.
    [ Google | Google Scholar ]
  10. OLIVEIRA, D.; CERQUEIRA, A.; OLIVEIRA, R. I. Counting communities in weighted Stochastic Block Models via semidefinite programming. 2025.
    [ Google | Google Scholar ]
  11. LUIZ, DENIS A.; COLETTI, CRISTIAN F. A Non-Markovian Approach to a Stochastic Rumor Dynamics with Cognitive Deliberation. 2025.
    [ Google | Google Scholar ]

2024

  1. ROCHA, M. G.; Garcia, Nancy L. Predicting Dengue Outbreaks: A Dynamic Approach with Variable Length Markov Chains and Exogenous Factors. 2024.
    [ Google | Google Scholar ]
  2. SOUSA, A. R. S. A class of priors to perform asymmetric Bayesian wavelet shrinkage. 2024.
    [ Google | Google Scholar ]
  3. CARVALHO, WALTER AUGUSTO FONSECA 'O PLANO ABC+, RENOVAGRO, PSA E GESTÃO FUNDIÁRIA: ESTRATÉGIAS PARA O ENFRENTAMENTO ÀS MUDANÇAS CLIMÁTICAS NO BRASIL,'. 2024.
    [ Google | Google Scholar ]
  4. CARVALHO, WALTER AUGUSTO FONSECA USO DE DIFERENTES ADJUVANTES NA DESSECAÇÃO DA CULTURA DE SOJA: AVALIAÇÃO DA QUALIDADE DAS SEMENTES. 2024.
    [ Google | Google Scholar ]
  5. CARVALHO, WALTER AUGUSTO FONSECA A AGROECOLOGIA E A BIODIVERSIDADE DE FEIJÕES. 2024.
    [ Google | Google Scholar ]
  6. CERQUEIRA, A.; COSTA, L. L. S. Modeling sparsity in count-weighted networks. 2024.
    [ Google | Google Scholar ]
  7. FREITAS, J. V. B.; AZEVEDO Bayesian inference for scale mixtures of skew-normal linear models under the centered parameterization. 2024.
    [ Google | Google Scholar ]
  8. FREITAS, J. V. B.; AZEVEDO Regression models for binary data with scale mixtures of centered skew-normal link functions. 2024.
    [ Google | Google Scholar ]
  9. JUSTUS, V. L.; RODRIGUES, Vitor Batista; Sousa, Alex R S Bootstrap confidence intervals: A comparative simulation study (arXiv:2404.12967). 2024.
    [ Google | Google Scholar ]
  10. GOMES, T. F. P.; Bauce, Rodrigo; SIMOES, R. Inteligência Artificial na Gestão de Risco de Crédito. 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 ]
  2. CARVALHO, WALTER AUGUSTO FONSECA CONTROLE ALTERNATIVO DE OÍDIO NA CULTURA DO PEPINO EM AMBIENTE PROTEGIDO. 2023.
    [ Google | Google Scholar ]
  3. CARVALHO, WALTER AUGUSTO FONSECA USO DE RPA PARA IDENTIFICAÇÃO DE FALHAS NA CULTURA DA SOJA. 2023.
    [ Google | Google Scholar ]
  4. CARVALHO, WALTER AUGUSTO FONSECA FONTES DE ÁGUA E QUALIDADE DO SOLO EM ÁREA IRRIGADA PARA PRODUÇÃO DO CAFÉ ROBUSTA (Coffea conephora) NA REGIÃO AMAZÔNICA: : estudo de caso na bacia hidrográfica do rio São Miguel do Guaporé ? RO. 2023.
    [ Google | Google Scholar ]
  5. PEREIRA, N. P.; OLIVEIRA, A.J. Aspectos teóricos e computacionais relacionados ao problema dos transportes. 2023.
    [ Google | Google Scholar ]
  6. BERNARDINELLI, P. F. G.; FLORINDO, J. B. Aprendizado profundo na previsão de séries temporais: uma análise comparativa. 2023.
    [ Google | Google Scholar ]
  7. CERQUEIRA, A.; LEONARDI, F. Optimal recovery by maximum and integrated conditional likelihood in the general Stochastic Block Model. 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 ]
  4. SANTOS, D. S. Modelos de regularização com imputação e curvas de decisão aplicados a dados de medicina.. 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. SOUSA, ALEX RODRIGO DOS S.; Garcia, Nancy L. Wavelet Shrinkage in Nonparametric Regression Models with Positive Noise. 2021.
    [ Google | Google Scholar ]
  3. 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 ]
  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. Singer, J.M.; Nobre, J.S.; ROCHA, F. M. M. Análise de Dados Longitudinais (versão parcial preliminar). 2021.
    [ Google | Google Scholar ]
  6. SOUSA, A. R. S. Kurtosis control in wavelet shrinkage with generalized secant hyperbolic prior. 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 ]
  3. SOUSA, A. R. S.; SEVERINO, M.; LEONARDI, F. Model selection criteria for regression models with splines and the automatic localization of knots. 2020.
    [ Google | Google Scholar ]
  4. LEME, H.; HANSEN, P. M.; EL, A. -. A. B. C. E. Boas Práticas em Gestão de Risco. 2020.
    [ Google | Google Scholar ]
  5. SANTOS, D. S. Um Modelo Baseado em Equações de Difusão para a Dinâmica do Preço de Ativos Financeiros.. 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. GONCALVES, B. O.; VACHKOVSKAIA, M. Explosion in a growth model with cooperative interaction on an infinite graph. 2019.
    [ Google | Google Scholar ]
  3. CERQUEIRA, A.; GARCIA, N. L. Graphical Construction of Spatial Gibbs Random Graphs. 2019.
    [ Google | Google Scholar ]
  4. ZAMBOM, A. Z.; KIM, S.; Garcia, Nancy L. Variable Length Markov Chain with Exogenous Covariates. 2019.
    [ Google | Google Scholar ]
  5. SOUSA, ALEX RODRIGO DOS S.; Garcia, Nancy L.; VIDAKOVIC, BRANI Bayesian Wavelet Shrinkage with Beta Priors. 2019.
    [ Google | Google Scholar ]
  6. SILVA, A. R. S. Item response theory models for augmented continuous-limited responses. 2019.
    [ Google | Google Scholar ]
  7. Hernandez-Velasco, Lina L.; Abanto-Valle, Carlos; DEY, DIPAK K. Mixed Effects State-Space Models with Student-t errors. 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 ]
  9. SANTOS, J. R. S.; AZEVEDO, C. L. N. A general Cholesky decomposition based modeling of longitudinal IRT data: Handling skewed latent traits distributions. 2017.
    [ Google | Google Scholar ]
  10. CORDER, V. A. O modelo de regressão Birnbaum-Sanders bivariado baseado na cópula FGM. 2017.
    [ Google | Google Scholar ]
  11. PIROUTEK, A.; ASSUNÇÃO, RENATO; DUARTE, D. Modelling Stochastic Neighborhood Structures in Disease Mapping. 2017.
    [ Google | Google Scholar ]
  12. PIROUTEK, A.; DUARTE, D.; ALVES, C.; ASSUNÇÃO, RENATO Probabilistic Context Neighborhood Model for Lattices in Z^2. 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 ]
  3. GARCIA, M. I.; GONZALEZ, L. F. P.; MORENO, G. E. Indice de Riesgo de Victimización 2015. 2016.
    [ Google | Google Scholar ]
  4. MORENO, G. E.; GONZALEZ, L. F. P. informe comparativo de índice de riesgo y de vulnerabilidad territorial para el postconflicto. 2016.
    [ Google | Google Scholar ]
  5. LABARRERE, C.; PIROUTEK, A.; MORRISSEY, C.; Bianchini, K; Hobson K Estimating immigration using clustering methods and kernel density estimate : Population structure of Sanderlings (Calidris alba) at a major stopover site in Chaplin Lake, Saskatchewan. 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 ]
  5. CASTRO, L. M.; GALVIS, D.M; Lachos, V.H; BANDYOPADHAY, D. Bayesian Semiparametric Linear Mixed Effects Models with Normal/Independent Distributions. 2015.
    [ Google | Google Scholar ]
  6. SILVA, A. R. S. Modelos de regressão beta retangular heteroscedásticos aumentados em zeros e uns. 2015.
    [ Google | Google Scholar ]
  7. CHAVES, N. L. Modelos de regressão Birnbaum-Saunders baseados na distribuição Normal assimétrica centrada.. 2015.
    [ Google | Google Scholar ]
  8. GONZALEZ, L. F. P.; GARCIA, M. I. Indice de Riesgo de Victimización. 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 ]
  2. CARVALHO, Walter Augusto Fonseca de; GARCIA, N. L.; GALLO, A. Continuity properties of a factor of Markov Chains.. 2014.
    [ Google | Google Scholar ]
  3. PRATES, M. O.; RODRIGUES, E. C.; ASSUNCAO, R. M. When is the spatial confounding a real problem? A fast Gaussian Markov random fields alternative to alleviate spatial confounding. 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 ]
  7. LB Sánchez, L Sánchez; DAVILA, V. H. L.; LABRA, F. V. Likelihood Based Inference for Quantile Regression Using the Asymmetric Laplace Distribution. 2013.
    [ Google | Google Scholar ]
  8. GONZALEZ, L. F. P.; CORRECHA, S.; RUBIO, J. Análisis de la deserción escolar en estudiantes que se encuentran en la niñez. 2013.
    [ Google | Google Scholar ]
  9. COSTA, D. R.; PRATES, M. O.; V. H. Lachos Generalized linear mixed models for correlated binary data with t-link. 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 ]
  5. PRATES, M. O.; Dey, Dipak K.; Lachos, Víctor Hugo A Dengue Fever Study in the State of Rio de Janeiro with the Use of Generalized Skew-Normal/Independent Spatial Fields. 2012.
    [ Google | Google Scholar ]
  6. MATOS, L. A.; Prates, Marcos O.; CHEN, M.; V. H. Lachos Likelihood Based Inference for Linear and Nonlinear Mixed-Effects Models with Censored Response Using the Multivariate-t Distribution. 2012.
    [ Google | Google Scholar ]
Arquivo gerado a partir dos currículos Lattes dos pesquisadores. Data do processamento: 22/07/2026 16:51:28
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