Model averaging based on Kullback-Leibler distance

Zhang, X; Zou, G; Carroll, RJ

Model averaging based on Kullback-Leibler distance

Zhang, X Zou, G Carroll, RJ

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Publication Type:: Journal Article
Citation:: Statistica Sinica, 2015, 25 (4), pp. 1583 - 1598
Issue Date:: 2015-10-01

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Field	Value	Language
dc.contributor.author	Zhang, X	en_US
dc.contributor.author	Zou, G	en_US
dc.contributor.author	Carroll, RJ	en_US
dc.date.issued	2015-10-01	en_US
dc.identifier.citation	Statistica Sinica, 2015, 25 (4), pp. 1583 - 1598	en_US
dc.identifier.issn	1017-0405	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119252
dc.description.abstract	© 2015, Institute of Statistical Science. All rights reserved. This paper proposes a model averaging method based on Kullback-Leibler distance under a homoscedastic normal error term. The resulting model average estimator is proved to be asymptotically optimal. When combining least squares estimators, the model average estimator is shown to have the same large sample properties as the Mallows model average (MMA) estimator developed by Hansen (2007). We show via simulations that, in terms of mean squared prediction error and mean squared parameter estimation error, the proposed model average estimator is more efficient than the MMA estimator and the estimator based on model selection using the corrected Akaike information criterion in small sample situations. A modified version of the new model average estimator is further suggested for the case of heteroscedastic random errors. The method is applied to a data set from the Hong Kong real estate market.	en_US
dc.relation.ispartof	Statistica Sinica	en_US
dc.relation.isbasedon	10.5705/ss.2013.326	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Model averaging based on Kullback-Leibler distance	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	25	en_US
utslib.for	0104 Statistics	en_US
utslib.for	0199 Other Mathematical Sciences	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	open_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	25	en_US

Abstract:

© 2015, Institute of Statistical Science. All rights reserved. This paper proposes a model averaging method based on Kullback-Leibler distance under a homoscedastic normal error term. The resulting model average estimator is proved to be asymptotically optimal. When combining least squares estimators, the model average estimator is shown to have the same large sample properties as the Mallows model average (MMA) estimator developed by Hansen (2007). We show via simulations that, in terms of mean squared prediction error and mean squared parameter estimation error, the proposed model average estimator is more efficient than the MMA estimator and the estimator based on model selection using the corrected Akaike information criterion in small sample situations. A modified version of the new model average estimator is further suggested for the case of heteroscedastic random errors. The method is applied to a data set from the Hong Kong real estate market.

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http://hdl.handle.net/10453/119252