Likelihood inferences for the link function without knowing the true underlying distributions

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/51874

Title:	Likelihood inferences for the link function without knowing the true underlying distributions
Authors:	Tsou,TS
Contributors:	統計研究所
Keywords:	LOGISTIC-REGRESSION MODELS;OF-FIT;STATISTICAL EVIDENCE;PARAMETRIC ROBUST;GRAPHICAL METHODS
Date:	2011
Issue Date:	2012-03-27 19:08:06 (UTC+8)
Publisher:	國立中央大學
Abstract:	This article is concerned with inference about link function in generalized linear models. A parametric and yet robust likelihood approach is introduced to accomplish the intended goal. More specifically, it is demonstrated that one can convert normal and gamma likelihoods into robust likelihood functions for the link function. The asymptotic validity of the robust likelihood requires only the existence of the second moments of the underlying distributions. The application of this novel robust likelihood method is demonstrated on the Box-Cox transformation. Simulation studies and real data analysis are provided to demonstrate the efficacy of the new parametric robust procedures.
Relation:	COMPUTATIONAL STATISTICS
Appears in Collections:	[Graduate Institute of Statistics] journal & Dissertation

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