Robust likelihood inference for regression parameters in partially linear models

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/51876

Title:	Robust likelihood inference for regression parameters in partially linear models
Authors:	Shen,CW;Tsou,TS;Balakrishnan,N
Contributors:	統計研究所
Keywords:	ESTIMATOR
Date:	2011
Issue Date:	2012-03-27 19:08:08 (UTC+8)
Publisher:	國立中央大學
Abstract:	A robust likelihood approach is proposed for inference about regression parameters in partially-linear models. More specifically, normality is adopted as the working model and is properly corrected to accomplish the objective. Knowledge about the true underlying random mechanism is not required for the proposed method. Simulations and illustrative examples demonstrate the usefulness of the proposed robust likelihood method, even in irregular situations caused by the components of the nonparametric smooth function in partially-linear models. (C) 2010 Elsevier B.V. All rights reserved.
Relation:	COMPUTATIONAL STATISTICS & DATA ANALYSIS
Appears in Collections:	[Graduate Institute of Statistics] journal & Dissertation

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