Study on the Lasso Method for Variable Selectionin Linear Regression Model with Mallows'' Cp

碩士 === 國立臺灣大學 === 數學研究所 === 95 === 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...

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Main Authors:	Hsin-Hsiung Huang, 黃信雄
Other Authors:	Hung Chen
Format:	Others
Language:	en_US
Published:	2006
Online Access:	http://ndltd.ncl.edu.tw/handle/41127770529976845884

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spelling	ndltd-TW-095NTU054790022015-12-11T04:04:49Z http://ndltd.ncl.edu.tw/handle/41127770529976845884 Study on the Lasso Method for Variable Selectionin Linear Regression Model with Mallows'' Cp 使用Lasso-Cp選取線性模型解釋變數之探討 Hsin-Hsiung Huang 黃信雄碩士國立臺灣大學數學研究所 95 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. Hung Chen 陳宏 2006 學位論文 ; thesis 46 en_US
collection	NDLTD
language	en_US
format	Others
sources	NDLTD
description	碩士 === 國立臺灣大學 === 數學研究所 === 95 === 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.
author2	Hung Chen
author_facet	Hung Chen Hsin-Hsiung Huang 黃信雄
author	Hsin-Hsiung Huang 黃信雄
spellingShingle	Hsin-Hsiung Huang 黃信雄 Study on the Lasso Method for Variable Selectionin Linear Regression Model with Mallows'' Cp
author_sort	Hsin-Hsiung Huang
title	Study on the Lasso Method for Variable Selectionin Linear Regression Model with Mallows'' Cp
title_short	Study on the Lasso Method for Variable Selectionin Linear Regression Model with Mallows'' Cp
title_full	Study on the Lasso Method for Variable Selectionin Linear Regression Model with Mallows'' Cp
title_fullStr	Study on the Lasso Method for Variable Selectionin Linear Regression Model with Mallows'' Cp
title_full_unstemmed	Study on the Lasso Method for Variable Selectionin Linear Regression Model with Mallows'' Cp
title_sort	study on the lasso method for variable selectionin linear regression model with mallows'' cp
publishDate	2006
url	http://ndltd.ncl.edu.tw/handle/41127770529976845884
work_keys_str_mv	AT hsinhsiunghuang studyonthelassomethodforvariableselectioninlinearregressionmodelwithmallowscp AT huángxìnxióng studyonthelassomethodforvariableselectioninlinearregressionmodelwithmallowscp AT hsinhsiunghuang shǐyònglassocpxuǎnqǔxiànxìngmóxíngjiěshìbiànshùzhītàntǎo AT huángxìnxióng shǐyònglassocpxuǎnqǔxiànxìngmóxíngjiěshìbiànshùzhītàntǎo
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Study on the Lasso Method for Variable Selectionin Linear Regression Model with Mallows'' Cp

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