Abstract #300986

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JSM 2003 Abstract #300986
Activity Number: 29
Type: Contributed
Date/Time: Sunday, August 3, 2003 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #300986
Title: Generalized Linear Mixed Models for Nominal Data
Author(s): Anders Skrondal*+ and Sophia Rabe-Hesketh
Companies: Norwegian Institute of Public Health and Kings College London
Address: PO Box 4404 Nydalen, Oslo, , N0403, Norway
Keywords: generalized linear mixed models ; multilevel models ; nominal data ; unordered polytomous data ; rankings
Abstract:

We propose a unifying framework for generalized linear mixed modeling of nominal data (unordered polytomous data and rankings), modeling dependence using factor and/or random coefficient structures at different hierarchical levels. The framework subsumes a wide range of models proposed in disparate methodological literatures. Partial and tied rankings, alternative specific explanatory variables and alternative sets varying across units are accommodated. Parameter estimation proceeds by maximum likelihood and prediction of factors/random coefficients by empirical Bayes, based on adaptive quadrature as implemented in the gllamm software. The methodology is applied to party choice and rankings from the 1987-1992 panel of the British Election Study. Three levels are considered: elections, voters, and constituencies.

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