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邓又在《中国科学:数学(英文版)》发表大作了。Model averaging for semiparametric additive partial linear
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2010-05Add to Favorite Get Latest Update Model averaging for semiparametric additive partial linear modelsDENG GuoHua1 & LIANG Hua21School of Finance and Statistics,Jiangxi University of Finance and Economics,Nanchang 330013,China 2Department of Biostatics and Computational Biology,University of Rochester,Rochester,NY 14642,USA
To improve the prediction accuracy of semiparametric additive partial linear models(APLM) and the coverage probability of confidence intervals of the parameters of interest,we explore a focused information criterion for model selection among ALPM after we estimate the nonparametric functions by the polynomial spline smoothing,and introduce a general model average estimator.The major advantage of the proposed procedures is that iterative backfitting implementation is avoided,which thus results in gains in computational simplicity.The resulting estimators are shown to be asymptotically normal.A simulation study and a real data analysis are presented for illustrations.
【Key Words】: backfitting focused information criterion polynomial spline model selection model uncertainty
【Fund】: supported by US National Science Foundation (Grant No.DMS-0806097)
【CateGory Index】: O212.1
【DOI】: CNKI:SUN:JAXG.0.2010-05-020
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