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Article: Shrinkage estimation of varying covariate effects based on quantile regression

TitleShrinkage estimation of varying covariate effects based on quantile regression
Authors
KeywordsAdaptive-LASSO
Censoring
Quantile regression
Shrinkage estimation
Variable selection
Varying covariate effects
Issue Date2014
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0960-3174
Citation
Statistics and Computing, 2014, v. 24, p. 853-869 How to Cite?
AbstractVarying covariate effects often manifest meaningful heterogeneity in covariate-response associations. In this paper, we adopt a quantile regression model that assumes linearity at a continuous range of quantile levels as a tool to explore such data dynamics. The consideration of potential non-constancy of covariate effects necessitates a new perspective for variable selection, which, under the assumed quantile regression model, is to retain variables that have effects on all quantiles of interest as well as those that influence only part of quantiles considered. Current work on l 1 -penalized quantile regression either does not concern varying covariate effects or may not produce consistent variable selection in the presence of covariates with partial effects, a practical scenario of interest. In this work, we propose a shrinkage approach by adopting a novel uniform adaptive LASSO penalty. The new approach enjoys easy implementation without requiring smoothing. Moreover, it can consistently identify the true model (uniformly across quantiles) and achieve the oracle estimation efficiency. We further extend the proposed shrinkage method to the case where responses are subject to random right censoring. Numerical studies confirm the theoretical results and support the utility of our proposals.
Persistent Identifierhttp://hdl.handle.net/10722/221663
ISSN
2015 Impact Factor: 1.786
2015 SCImago Journal Rankings: 1.993
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorPeng, L-
dc.contributor.authorXu, J-
dc.contributor.authorKutner, N-
dc.date.accessioned2015-12-04T15:28:58Z-
dc.date.available2015-12-04T15:28:58Z-
dc.date.issued2014-
dc.identifier.citationStatistics and Computing, 2014, v. 24, p. 853-869-
dc.identifier.issn0960-3174-
dc.identifier.urihttp://hdl.handle.net/10722/221663-
dc.description.abstractVarying covariate effects often manifest meaningful heterogeneity in covariate-response associations. In this paper, we adopt a quantile regression model that assumes linearity at a continuous range of quantile levels as a tool to explore such data dynamics. The consideration of potential non-constancy of covariate effects necessitates a new perspective for variable selection, which, under the assumed quantile regression model, is to retain variables that have effects on all quantiles of interest as well as those that influence only part of quantiles considered. Current work on l 1 -penalized quantile regression either does not concern varying covariate effects or may not produce consistent variable selection in the presence of covariates with partial effects, a practical scenario of interest. In this work, we propose a shrinkage approach by adopting a novel uniform adaptive LASSO penalty. The new approach enjoys easy implementation without requiring smoothing. Moreover, it can consistently identify the true model (uniformly across quantiles) and achieve the oracle estimation efficiency. We further extend the proposed shrinkage method to the case where responses are subject to random right censoring. Numerical studies confirm the theoretical results and support the utility of our proposals.-
dc.languageeng-
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0960-3174-
dc.relation.ispartofStatistics and Computing-
dc.subjectAdaptive-LASSO-
dc.subjectCensoring-
dc.subjectQuantile regression-
dc.subjectShrinkage estimation-
dc.subjectVariable selection-
dc.subjectVarying covariate effects-
dc.titleShrinkage estimation of varying covariate effects based on quantile regression-
dc.typeArticle-
dc.identifier.emailXu, J: xujf@hku.hk-
dc.identifier.authorityXu, J=rp02086-
dc.identifier.doi10.1007/s11222-013-9406-4-
dc.identifier.scopuseid_2-s2.0-84904267989-
dc.identifier.volume24-
dc.identifier.spage853-
dc.identifier.epage869-
dc.identifier.isiWOS:000339380000012-

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