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Master's Dissertation
DOI
10.11606/D.45.2018.tde-23082018-210710
Document
Author
Full name
Matheus Augustus Pumputis Marques
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2018
Supervisor
Committee
Elian, Silvia Nagib (President)
Artes, Rinaldo
Sato, João Ricardo
Title in Portuguese
Análise e comparação de alguns métodos alternativos de seleção de variáveis preditoras no modelo de regressão linear
Keywords in Portuguese
FSR
LARS
LASSO Bayesiano
Modelos lineares
NAMS
Regressão linear
RFS
Seleção de modelos
Seleção de variáveis
Spike-and-Slab LASSO
Abstract in Portuguese
Neste trabalho estudam-se alguns novos métodos de seleção de variáveis no contexto da regressão linear que surgiram nos últimos 15 anos, especificamente o LARS - Least Angle Regression, o NAMS - Noise Addition Model Selection, a Razão de Falsa Seleção - RFS (FSR em inglês), o LASSO Bayesiano e o Spike-and-Slab LASSO. A metodologia foi a análise e comparação dos métodos estudados e aplicações. Após esse estudo, realizam-se aplicações em bases de dados reais e um estudo de simulação, em que todos os métodos se mostraram promissores, com os métodos Bayesianos apresentando os melhores resultados.
Title in English
Analysis and comparison of some alternative methods of selection of predictor variables in linear regression models.
Keywords in English
Bayesian LASSO
FSR
LARS
Linear Models
Linear Regression
Model Selection
NAMS
Spike-and-Slab LASSO
Variable Selection
Abstract in English
In this work, some new variable selection methods that have appeared in the last 15 years in the context of linear regression are studied, specifically the LARS - Least Angle Regression, the NAMS - Noise Addition Model Selection, the False Selection Rate - FSR, the Bayesian LASSO and the Spike-and-Slab LASSO. The methodology was the analysis and comparison of the studied methods. After this study, applications to real data bases are made, as well as a simulation study, in which all methods are shown to be promising, with the Bayesian methods showing the best results.
 
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Publishing Date
2018-08-29
 
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