Variable selection in a partially linear proportional hazards model with a diverging dimensionality

Variable selection in a partially linear proportional hazards model with a diverging dimensionality

We consider the problem of simultaneous variable selection and estimation in partially linear proportional hazards models when the number of covariates in the linear part diverges with the sample size. We apply the smoothly clipped absolute deviation (SCAD) penalty to select the significant covariat...

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Main Authors: Hu, Yuao, Lian, Heng
Other Authors: School of Physical and Mathematical Sciences
Format: Article
Language:English
Published: 2013
Online Access:https://hdl.handle.net/10356/103850
http://hdl.handle.net/10220/16962
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1038502020-03-07T12:34:42Z Variable selection in a partially linear proportional hazards model with a diverging dimensionality Hu, Yuao Lian, Heng School of Physical and Mathematical Sciences We consider the problem of simultaneous variable selection and estimation in partially linear proportional hazards models when the number of covariates in the linear part diverges with the sample size. We apply the smoothly clipped absolute deviation (SCAD) penalty to select the significant covariates in the linear part. Some simulations and a real data set are presented. 2013-10-28T03:46:14Z 2019-12-06T21:21:32Z 2013-10-28T03:46:14Z 2019-12-06T21:21:32Z 2012 2012 Journal Article Hu, Y., & Lian, H. (2013). Variable selection in a partially linear proportional hazards model with a diverging dimensionality. Statistics & Probability Letters, 83(1), 61-69. 0167-7152 https://hdl.handle.net/10356/103850 http://hdl.handle.net/10220/16962 10.1016/j.spl.2012.08.024 en Statistics & probability letters
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
description We consider the problem of simultaneous variable selection and estimation in partially linear proportional hazards models when the number of covariates in the linear part diverges with the sample size. We apply the smoothly clipped absolute deviation (SCAD) penalty to select the significant covariates in the linear part. Some simulations and a real data set are presented.
author2 School of Physical and Mathematical Sciences
author_facet School of Physical and Mathematical Sciences
Hu, Yuao
Lian, Heng
format Article
author Hu, Yuao
Lian, Heng
spellingShingle Hu, Yuao
Lian, Heng
Variable selection in a partially linear proportional hazards model with a diverging dimensionality
author_sort Hu, Yuao
title Variable selection in a partially linear proportional hazards model with a diverging dimensionality
title_short Variable selection in a partially linear proportional hazards model with a diverging dimensionality
title_full Variable selection in a partially linear proportional hazards model with a diverging dimensionality
title_fullStr Variable selection in a partially linear proportional hazards model with a diverging dimensionality
title_full_unstemmed Variable selection in a partially linear proportional hazards model with a diverging dimensionality
title_sort variable selection in a partially linear proportional hazards model with a diverging dimensionality
publishDate 2013
url https://hdl.handle.net/10356/103850
http://hdl.handle.net/10220/16962
_version_ 1681048239081521152

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