Deletion diagnostics for generalized linear models using the adjusted Poisson likelihood function

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/51083

Title:	Deletion diagnostics for generalized linear models using the adjusted Poisson likelihood function
Authors:	Chien,LC;Tsou,TS
Contributors:	系統生物與生物資訊研究所
Keywords:	REGRESSION
Date:	2011
Issue Date:	2012-03-27 18:20:55 (UTC+8)
Publisher:	國立中央大學
Abstract:	In this article, we propose two novel diagnostic measures for the deletion of influential observations for regression parameters in the setting of generalized linear models. The proposed diagnostic methods are capable for detecting the influential observations under model misspecification, as long as the true underlying distributions have finite second moments. More specifically, it is demonstrated that the Poisson likelihood function can be properly adjusted to become asymptotically valid for practically all underlying discrete distributions. The adjusted Poisson regression model that achieves the robustness property is presented. Simulation studies and an illustration are performed to demonstrate the efficacy of the two novel diagnostic procedures. (C) 2010 Elsevier B.V. All rights reserved.
Relation:	JOURNAL OF STATISTICAL PLANNING AND INFERENCE
Appears in Collections:	[Institute of Systems Biology and Bioinformatics] journal & Dissertation

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