Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/12257
Title: Geometria dos métodos de regressão LARS, LASSO e Elastic Net com uma aplicação em seleção genômica
Authors: Chaves, Lucas Monteiro
Chaves, Lucas Monteiro
Sáfadi, Thelma
Ferreira, Daniel Furtado
Nunes, José Airton Rodrigues
Silva, Fabyano Fonseca e
Keywords: Regressão linear – Geometria
Linear regression – Geometry
Least Absolute Shrinkage and Selection Operator (LASSO)
Least Angle Regression (LARS)
Elastic net
Issue Date: 6-Feb-2017
Publisher: Universidade Federal de Lavras
Citation: PEREIRA, L. da S. Geometria dos métodos de regressão LARS, LASSO e Elastic Net com uma aplicação em seleção genômica. 2017. 167 p. Tese (Doutorado em Estatística e Experimentação Agropecuária)-Universidade Federal de Lavras, Lavras, 2017.
Abstract: The methods of estimation and variable selection in linear models, LASSO (Least Absolute Shrinkage and Selection Operator), LARS (Least Angle Regression) and Elastic Net, are addressed using emphasis in terms of a geometric approach. The present work proposes to be an auxiliary reading to the classic articles of Tibshirani, Hastie, Efron, Zou and Johnstone, presenting in more detail some of the results cited in such articles. Such a point of view does not occur in the basic literature regarding these methods, and in this sense the work represents an original contribution to the subject. Simulations using R code (glmnet package) are developed to study the behavior of the estimators. For a data set of Sus scrofa pork, 237 genetic markers (SNPs) are analyzed using the Elastic Net method, using as response the pork pH and carcass weight.
URI: http://repositorio.ufla.br/jspui/handle/1/12257
Appears in Collections:DES - Estatística e Experimentação Agropecuária - Doutorado (Teses)



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