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Article: Regression coefficient and autoregressive order shrinkage and selection via the lasso

TitleRegression coefficient and autoregressive order shrinkage and selection via the lasso
Authors
KeywordsAutoregression with exogenous variables
Lasso
Oracle estimator
Regression model with autoregressive errors
Issue Date2007
PublisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/RSSB
Citation
Journal Of The Royal Statistical Society. Series B: Statistical Methodology, 2007, v. 69 n. 1, p. 63-78 How to Cite?
AbstractThe least absolute shrinkage and selection operator ('lasso') has been widely used in regression shrinkage and selection. We extend its application to the regression model with autoregressive errors. Two types of lasso estimators are carefully studied. The first is similar to the traditional lasso estimator with only two tuning parameters (one for regression coefficients and the other for autoregression coefficients). These tuning parameters can be easily calculated via a data-driven method, but the resulting lasso estimator may not be fully efficient. To overcome this limitation, we propose a second lasso estimator which uses different tuning parameters for each coefficient. We show that this modified lasso can produce the estimator as efficiently as the oracle. Moreover, we propose an algorithm for tuning parameter estimates to obtain the modified lasso estimator. Simulation studies demonstrate that the modified estimator is superior to the traditional estimator. One empirical example is also presented to illustrate the usefulness of lasso estimators. The extension of the lasso to the autoregression with exogenous variables model is briefly discussed. © 2007 Royal Statistical Society.
Persistent Identifierhttp://hdl.handle.net/10722/83053
ISSN
2015 Impact Factor: 4.222
2015 SCImago Journal Rankings: 7.429
References

 

DC FieldValueLanguage
dc.contributor.authorHansheng, Wen_HK
dc.contributor.authorGuodong, Len_HK
dc.contributor.authorTsai, CLen_HK
dc.date.accessioned2010-09-06T08:36:23Z-
dc.date.available2010-09-06T08:36:23Z-
dc.date.issued2007en_HK
dc.identifier.citationJournal Of The Royal Statistical Society. Series B: Statistical Methodology, 2007, v. 69 n. 1, p. 63-78en_HK
dc.identifier.issn1369-7412en_HK
dc.identifier.urihttp://hdl.handle.net/10722/83053-
dc.description.abstractThe least absolute shrinkage and selection operator ('lasso') has been widely used in regression shrinkage and selection. We extend its application to the regression model with autoregressive errors. Two types of lasso estimators are carefully studied. The first is similar to the traditional lasso estimator with only two tuning parameters (one for regression coefficients and the other for autoregression coefficients). These tuning parameters can be easily calculated via a data-driven method, but the resulting lasso estimator may not be fully efficient. To overcome this limitation, we propose a second lasso estimator which uses different tuning parameters for each coefficient. We show that this modified lasso can produce the estimator as efficiently as the oracle. Moreover, we propose an algorithm for tuning parameter estimates to obtain the modified lasso estimator. Simulation studies demonstrate that the modified estimator is superior to the traditional estimator. One empirical example is also presented to illustrate the usefulness of lasso estimators. The extension of the lasso to the autoregression with exogenous variables model is briefly discussed. © 2007 Royal Statistical Society.en_HK
dc.languageengen_HK
dc.publisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/RSSBen_HK
dc.relation.ispartofJournal of the Royal Statistical Society. Series B: Statistical Methodologyen_HK
dc.subjectAutoregression with exogenous variablesen_HK
dc.subjectLassoen_HK
dc.subjectOracle estimatoren_HK
dc.subjectRegression model with autoregressive errorsen_HK
dc.titleRegression coefficient and autoregressive order shrinkage and selection via the lassoen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0964-1998&volume=69&spage=63&epage=78&date=2007&atitle=Regression+Coefficients+And+Autoregressive+Order+Shrinkage+And+Selection+Via+The+Lassoen_HK
dc.identifier.emailGuodong, L: gdli@hku.hken_HK
dc.identifier.authorityGuodong, L=rp00738en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1111/j.1467-9868.2007.00577.xen_HK
dc.identifier.scopuseid_2-s2.0-33846190566en_HK
dc.identifier.hkuros158548en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33846190566&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume69en_HK
dc.identifier.issue1en_HK
dc.identifier.spage63en_HK
dc.identifier.epage78en_HK
dc.identifier.eissn1467-9868-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridHansheng, W=26654911600en_HK
dc.identifier.scopusauthoridGuodong, L=52563850500en_HK
dc.identifier.scopusauthoridTsai, CL=15766757300en_HK
dc.identifier.citeulike1048319-

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