Variable selection in high-dimensional partly linear additive models

Semiparametric models are particularly useful for high-dimensional regression problems. In this paper, we focus on partly linear additive models with a large number of predictors (can be larger than the sample size) and consider model estimation and variable selection based on polynomial spline expa...

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Main Author:	Lian, Heng
Other Authors:	School of Physical and Mathematical Sciences
Format:	Article
Language:	English
Published:	2013
Subjects:	DRNTU::Science::Mathematics::Statistics
Online Access:	https://hdl.handle.net/10356/97695 http://hdl.handle.net/10220/17096
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Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-97695
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spelling	sg-ntu-dr.10356-976952020-03-07T12:31:33Z Variable selection in high-dimensional partly linear additive models Lian, Heng School of Physical and Mathematical Sciences DRNTU::Science::Mathematics::Statistics Semiparametric models are particularly useful for high-dimensional regression problems. In this paper, we focus on partly linear additive models with a large number of predictors (can be larger than the sample size) and consider model estimation and variable selection based on polynomial spline expansion for the nonparametric part with adaptive lasso penalty on the linear part. Convergence rates as well as asymptotic normality of the linear part are shown. We also perform some Monte Carlo studies to demonstrate the performance of the estimator. 2013-10-31T01:43:18Z 2019-12-06T19:45:33Z 2013-10-31T01:43:18Z 2019-12-06T19:45:33Z 2012 2012 Journal Article Lian, H. (2012). Variable selection in high-dimensional partly linear additive models. Journal of nonparametric statistics, 24(4), 825-839. https://hdl.handle.net/10356/97695 http://hdl.handle.net/10220/17096 10.1080/10485252.2012.701300 en Journal of nonparametric statistics
institution	Nanyang Technological University
building	NTU Library
country	Singapore
collection	DR-NTU
language	English
topic	DRNTU::Science::Mathematics::Statistics
spellingShingle	DRNTU::Science::Mathematics::Statistics Lian, Heng Variable selection in high-dimensional partly linear additive models
description	Semiparametric models are particularly useful for high-dimensional regression problems. In this paper, we focus on partly linear additive models with a large number of predictors (can be larger than the sample size) and consider model estimation and variable selection based on polynomial spline expansion for the nonparametric part with adaptive lasso penalty on the linear part. Convergence rates as well as asymptotic normality of the linear part are shown. We also perform some Monte Carlo studies to demonstrate the performance of the estimator.
author2	School of Physical and Mathematical Sciences
author_facet	School of Physical and Mathematical Sciences Lian, Heng
format	Article
author	Lian, Heng
author_sort	Lian, Heng
title	Variable selection in high-dimensional partly linear additive models
title_short	Variable selection in high-dimensional partly linear additive models
title_full	Variable selection in high-dimensional partly linear additive models
title_fullStr	Variable selection in high-dimensional partly linear additive models
title_full_unstemmed	Variable selection in high-dimensional partly linear additive models
title_sort	variable selection in high-dimensional partly linear additive models
publishDate	2013
url	https://hdl.handle.net/10356/97695 http://hdl.handle.net/10220/17096
_version_	1681046786389573632

Variable selection in high-dimensional partly linear additive models

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