A Nonlinear Gauss-Seidel Algorithm for Inference About Glmm

A nonlinear Gauss-Seidel type algorithm is proposed for computing the maximum posterior estimates of the random effects in a generalized linear mixed model. We show that the algorithm converges in virtually all typical situations of generalized linear mixed models. A numerical example shows the superiority of the proposed algorithm over the standard Newton-Raphson procedure when the number of random effects is large

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Year of publication:	2000
Authors:	Jiang, Jiming
Publisher:	[S.l.] : SSRN
Subject:	Algorithmus \| Algorithm \| Theorie \| Theory \| Nichtlineare Regression \| Nonlinear regression \| Induktive Statistik \| Statistical inference
Description of contents:	Abstract [papers.ssrn.com]

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Extent:	1 Online-Ressource
Type of publication:	Book / Working Paper
Language:	English
Notes:	In: Computational Statistics, Vol. 15, Issue 2, 2000 Volltext nicht verfügbar
Classification:	C19 - Econometric and Statistical Methods: General. Other
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10014151775