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Master's Dissertation
DOI
https://doi.org/10.11606/D.104.2019.tde-06082019-170631
Document
Author
Full name
Rafael de Carvalho Ceregatti de Console
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2018
Supervisor
Committee
Salasar, Luis Ernesto Bueno (President)
Lopes, Danilo Lourenço
Souza, Anderson Luiz Ara
Title in English
A Bayesian nonparametric approach for the two-sample problem
Keywords in English
Bayesian nonparametrics
Dirichlet process
Hypothesis testing
Two-sample problem
Abstract in English
In this work, we discuss the so-called two-sample problem Pearson and Neyman (1930) assuming a nonparametric Bayesian approach. Considering X1; : : : ; Xn and Y1; : : : ; Ym two independent i.i.d samples generated from P1 and P2, respectively, the two-sample problem consists in deciding if P1 and P2 are equal. Assuming a nonparametric prior, we propose an evidence index for the null hypothesis H0 : P1 = P2 based on the posterior distribution of the distance d (P1; P2) between P1 and P2. This evidence index has easy computation, intuitive interpretation and can also be justified in the Bayesian decision-theoretic context. Further, in a Monte Carlo simulation study, our method presented good performance when compared with the well known Kolmogorov- Smirnov test, the Wilcoxon test as well as a recent testing procedure based on Polya tree process proposed by Holmes (HOLMES et al., 2015). Finally, we applied our method to a data set about scale measurements of three different groups of patients submitted to a questionnaire for Alzheimer's disease diagnostic.
Title in Portuguese
Uma abordagem bayesiana não paramétrica para o problema de duas amostras
Keywords in Portuguese
Bayesiano Não-paramétrico
Problema de Duas Amostras
Processo de Dirichlet
Teste de Hipótese
Abstract in Portuguese
Neste trabalho, discutimos o problema conhecido como problema de duas amostras Pearson and Neyman (1930) utilizando uma abordagem bayesiana não-paramétrica. Considere X1; : : : ; Xn and Y1; : : : ;Ym duas amostras independentes, geradas por P1 e P2, respectivamente, o problema de duas amostras consiste em decidir se P1 e P2 são iguais. Assumindo uma priori não-paramétrica, propomos um índice de evidência para a hipótese nula H0 : P1 = P2 baseado na distribuição a posteriori da distância d (P1; P2) entre P1 e P2. O índice de evidência é de fácil implementação, tem uma interpretação intuitiva e também pode ser justificada no contexto da teoria da decisão bayesiana. Além disso, em um estudo de simulação de Monte Carlo, nosso método apresentou bom desempenho quando comparado com o teste de Kolmogorov-Smirnov, com o teste de Wilcoxon e com o método de Holmes. Finalmente, aplicamos nosso método em um conjunto de dados sobre medidas de escala de três grupos diferentes de pacientes submetidos a um questionário para diagnóstico de doença de Alzheimer.
 
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Publishing Date
2019-08-07
 
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