Browsing Teses e dissertações by Title

Now showing items 122-141 of 158

    • Neural networks as an optimization tool for regression 

      Coscrato, Victor Azevedo (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 02/09/2019)
      Neural networks are a tool to solve prediction problems that have gained much prominence recently. In general, neural networks are used as a predictive method, that is, their are used to estimate a regression function. ...

    • A new class of discrete models for the analysis of zero-modified count data 

      Silva, Wesley Bertoli da (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 03/04/2020)
      In this work, a new class of discrete models for the analysis of zero-modified count data has been introduced. The proposed class is composed of hurdle versions of the Poisson-Lindley, Poisson-Shanker, and Poisson-Sujatha ...

    • New families of linear and partially linear quantile regression models under reparameterized Marshall-Olkin distributions 

      Cortés, Isaac (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 31/07/2023)
      In this dissertation, we propose families of linear and partially linear quantile regression models, where the response variable follows a reparameterized Marshall-Olkin distribution with support on the real line. This ...

    • A nonparametric bayesian approach for modeling and comparison of functional data 

      Moreira, Diogo Barboza (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 02/09/2022)
      The current advances of technology provides, among other things, several ways of collecting data, which enlarges the possibility of studying new phenomena. Researches focused on studying the functional relation between ...

    • Nonparametric pragmatic hypothesis testing 

      Lassance, Rodrigo Ferrari Lucas (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 13/06/2022)
      In statistical testing, a pragmatic hypothesis is an extension of a precise one, taking cases on the vicinity of the null as being equally worthy of appraisal. Unlike standard procedures, pragmatic hypotheses allow the ...

    • Nova classe de modelos paramétricos para análise de sistemas reparáveis 

      Lopes, Tito Lívio da Cunha (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 13/01/2023)
      The Arithimetic Reduction of Age (ARA) model class from Doyen e Gaudoin (2004) has been widely used in the analysis of repairable systems, whose repair effect is expressed by an arithmetic age reduction. However, the ...

    • Um novo modelo de sobrevivência Bell-Inversa Gaussiana com fração de cura 

      Carregari, Renata Cristina (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 26/03/2021)
      In this work we propose a new survival model called the Bell-Inverse Gaussian cure rate. We consider different activation schemes in which the number of factors $M$ has the Bell distribution and the time of occurrence ...

    • Observações atípicas em alta dimensão 

      Hisatugu, Matheus Toshio (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 15/09/2022)
      Outliers and heteroskedastic noise are two common situations in Statistics. Nowadays the amount of generated data is very high and for this reason it is possible to find high dimensional data (the dimension d is just as ...

    • Penalized regression methods for compositional data 

      Shimizu, Taciana Kisaki Oliveira (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 10/12/2018)
      Compositional data consist of known vectors such as compositions whose components are positive and defined in the interval (0,1) representing proportions or fractions of a "whole", where the sum of these components must ...

    • Percolação Acessível 

      Assis, Ricardo de Jesus Caldas (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 17/02/2020)
      We consider the accessibility percolation model on the n-ary tree height the _nite h. The model is de_ned by associating a continuous random variable X_v for each vertex v in the tree. The main issue to consider and ...

    • Poincaré recurrence times in stochastic mixing processes 

      Amorim, Vitor Gustavo de (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 17/02/2022)
      In the context of the discrete-time stochastic processes, this thesis presents new results on Poincaré recurrence theory. After a complete review of recent results, we present a new theorem on the exponential approximations ...

    • Um procedimento para seleção de variáveis em modelos lineares generalizados duplos 

      Cavalaro, Lucas Leite (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 01/04/2019)
      The double generalized linear models (DGLM), unlike the generalized linear model (GLM), allow the fit of the dispersion parameter of the response variable as a function of predictor variables, improving the way of modeling ...

    • Processo de Bernoulli correlacionado 

      Novaes, Ricardo De Carli (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 28/06/2019)
      The independent Bernoulli process, which is a sequence of independent Bernoulli random variables, is already widely known in the statistical literature. This masters thesis works with a generalization of this process: the ...

    • Programação linear aplicada a estatística 

      Jesus, Alan Henrique de (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 27/11/2017)
      Determine probabilities for events where we have few information or intervals for probabilities is not so simple. For this we will develop concepts of linear programming, which allows us to solve, in a certain way, the ...

    • Propagação de rumor em uma população cética em N 

      Higashizawa, Lissa Kido (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 30/03/2023)
      We consider two models for information propagation in N. In both models, the individuals (one per site of N) have random, independent, and equally distributed radius. At the beginning only the individual at 0 has the ...

    • Quantificação em problemas com mudança de domínio 

      Vaz, Afonso Fernandes (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 17/05/2018)
      Several machine learning applications use classifiers as a way of quantifying the prevalence of positive class labels in a target dataset, a task named quantification. For instance, a naive way of determining what proportion ...

    • Redes Bayesianas para classificação com aprendizado via scoring and restrict: método, aplicação e comparação com métodos tradicionais 

      Ozelame, Camila Sgarioni (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 05/04/2021)
      This work is an investigation towards the behavior of discrete Bayesian Networks (BN) which aims to solve classification problems. This methodology is based on graphs and probability theories, and it is defined to be ...

    • Redes neurais para grafos e suas aplicações aos sistemas complexos 

      Carvalho, Guilherme Michel Lima de (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 08/04/2022)
      Complex systems are composed of several components that interact with each other. A natural approach for these types of systems is to use mathematical graph abstraction. In different contexts in the real world, it is ...

    • Regressão binária nas abordagens clássica e bayesiana 

      Fernandes, Amélia Milene Correia (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 16/12/2016)
      The objective of this work is to study the binary regression model under the frequentist and Bayesian approaches using the probit, logit, log-log complement, Box-Cox transformation and skewprobit as link functions. In the ...

    • Regressão binária usando ligações potência e reversa de potência 

      Chumbimune Anyosa, Susan Alicia (Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 07/04/2017)
      The aim of this dissertation is to study a family of asymmetric link functions for binary regression models under Bayesian approach. Specifically, we present the estimation of parameters of power and reversal power binary ...