Study on the Lasso Method for Variable Selection in Linear Regression Model with Mallows' Cp
Date Issued
2006
Date
2006
Author(s)
Huang, Hsin-Hsiung
DOI
en-US
Abstract
When the number of predictors in a linear regression model is large, regularization is a commonly used method to reduce the complexity of the fitted model. LASSO (Tibshirani, 1996) is being advocated as a useful regulation
method for achieving sparsity or parsimony of resulting fitted model. In this thesis, we study the operating characteristics of LASSO coupled with Mallows’Cp on identifying the orthonormal predictor variables of linear regression when the number of predictors and the number of the observation are of the same magnitude. The characteristics includes the chosen number of predictors and the proportion of correctly identified predictors. This result can be useful in multiple testing.
Subjects
最小角度回歸
Least angle regression
Forward selection
Type
thesis
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