• Bandas de predição usando densidade condicional estimada e um modelo LDA com covariáveis 

      Shimizu, Gilson Yuuji (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 15/10/2021)
      Machine learning methods are divided into two main groups: supervised and unsupervised methods. In the first part of this work, we develop a method for creating prediction bands that can be applied to supervised problems. ...
    • Bayesian and classical inference for the generalized gamma distribution and related models 

      Ramos, Pedro Luiz (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 22/02/2018)
      The generalized gamma (GG) distribution is an important model that has proven to be very flexible in practice for modeling data from several areas. This model has important sub-models, such as the Weibull, gamma, lognormal, ...
    • Bayesian estimation of dynamic mixture models by wavelets 

      Motta, Flávia Castro (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 20/04/2023)
      Gaussian mixture models are used successfully in various statistical learning applications. The good results provided by these models encourage several generalizations of them. Among possible adaptations, one can assume a ...
    • Bayesian inference for term structure models 

      Martins, Thomas Correa e Silva (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 09/06/2022)
      We explore recent advances in Bayesian methods in order to estimate the Vasicek, CIR and dynamic Nelson-Siegel (DNS) models for term structure of interest rates. The models are specified as state space time series. The ...
    • A bayesian nonparametric approach for the two-sample problem 

      Console, Rafael de Carvalho Ceregatti de (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 19/11/2018)
      In this work, we discuss the so-called two-sample problem (PEARSON; NEYMAN, 1930) assuming a nonparametric Bayesian approach. Considering X 1 ,...,X n and Y 1 ,...,Y m two inde- pendent i.i.d samples generated from P 1 ...
    • Bayesian spatial process models for activation patterns in transcranial magnetic stimulation mapping 

      Egbon, Osafu Augustine (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 07/07/2023)
      In recent years, Spatial statistical models have been gaining rapid attention for solving problems in biological systems due to the improvement in spatial data collection. It has proven extremely important in unveiling ...
    • Bayesian variable selection for logistic mixture models with Pólya-Gamma data augmentation 

      Bogoni, Mariella Ananias (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 15/02/2022)
      In this work, Bayesian methods for estimating and selecting variables in a mixture of logistic regressions model are presented. In order to simplify its Bayesian estimation, we extend the data augmentation approach ...
    • Bayesian variable selection using data driven reversible jump: an application to schizophrenia data 

      Montcho, Djidenou Hans Amos (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 17/12/2021)
      Symptom based diagnosis are known to be limited specially concerning complex disorders such as schizophrenia. Modern attempts in providing predictive risk for such disease, to assist existing diagnosis tools, integrate ...
    • Cadeias estocásticas de memória ilimitada com aplicação na neurociência 

      Ferreira, Ricardo Felipe (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 21/03/2019)
      Stochastic chains with unbounded memory are a natural generalization of Markov chains, in the sense that the transition probabilities may depend on the whole past. These process, introduced independently by Onicescu and ...
    • Classe de modelos de fragilidade com efeito do acúmulo de reparos em múltiplos sistemas reparáveis 

      D'Andrea, Amanda Morales Eudes (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 13/12/2019)
      In repairable systems, a fundamental aspect to be considered is predict the reliability of the systems being studied. However, the standard methods used to analyze reparable system data ignore the cumulative effect of ...
    • Comparação de métodos de estimação para problemas com colinearidade e/ou alta dimensionalidade (p > n) 

      Casagrande, Marcelo Henrique (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 29/04/2016)
      This paper presents a comparative study of the predictive power of four suitable regression methods for situations in which data, arranged in the planning matrix, are very poorly multicolinearity and / or high dimensionality, ...
    • Comparing two populations using Bayesian Fourier series density estimation 

      Inacio, Marco Henrique de Almeida (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 12/04/2017)
      Given two samples from two populations, one could ask how similar the populations are, that is, how close their probability distributions are. For absolutely continuous distributions, one way to measure the proximity of ...
    • Conditional independence testing, two sample comparison and density estimation using neural networks 

      Inácio, Marco Henrique de Almeida (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 03/08/2020)
      Given the vast amount of data available nowadays and the rapid increase of computational processing power, the field of machine learning and the so called algorithmic modeling have seen a recent surge in its popularity and ...
    • Conectividade do grafo aleatório de Erdös-Rényi e uma variante com conexões locais 

      Bedia, Elizbeth Chipa (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 24/03/2016)
      We say that a graph is connected if there is a path edges between any pair of vertices. Random graph Erd os-R enyi with n vertices is obtained by connecting each pair of vertex with probability pn 2 (0; 1) independently ...
    • Uma construção de cópulas semilineares bivariadas baseada nas famílias AMH, FGM e Plackett 

      Correia, Átila Prates (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 26/02/2021)
      Copulas are cumulative distribution functions defined on the unit square whose margins are uniform. In the context of the present work, we have provided three new families of semilinear copulas based on the copulas AMH, ...
    • Contribuições em modelos de regressão com erro de medida multiplicativo 

      Silva, Eveliny Barroso da (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 04/02/2016)
      In regression models in which a covariate is measured with error, it is common to use structures that correlate the observed covariate with the true non-observed covariate. Such structures are usually additive or ...
    • Contribuições para modelos de diagnóstico cognitivo 

      Oliveira, Eduardo Schneider Bueno de (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 23/02/2021)
      Cognitive Diagnostic Models (CDMs) are latent variable models which are useful for identifying the profile of respondents through tests or assessments. They are mainly used in educational assessments, but can also be ...
    • Contribuições sobre o envelope simulado na análise de diagnóstico em modelos de regressão 

      Fernandes, Victor Vinicius (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 30/04/2019)
      The simulated envelope is a diagnostic analysis method used to evaluate the hypothesis about the probability distribution assumed for the response variable in a regression model. In this work, we describe some procedures ...
    • Controle de sistemas não-Markovianos. 

      Souza, Francys Andrews de (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 13/09/2017)
      In this thesis, we present a concrete methodology to calculate the epsilon-optimal controls for non-Markovian stochastic systems. A pathwise analysis and the use of the discretization structure proposed by Leão and Ohash ...
    • O corte do FBST em modelos de alta dimensionalidade 

      Poloniato Ferreira, Joao Carlos (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 03/12/2018)
      The problem of controlling the significance level of the FBST (Full Bayesian Significant Test) test is studied in the context of Bayesian models for density, thus, a Bayesian method is shown that works with density ...