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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/2206

Title: A pairwise likelihood approach to estimation in multilevel probit models
Authors: Renard, Didier
Molenberghs, Geert
Geys, Helena
Issue Date: 2004
Citation: COMPUTATIONAL STATISTICS & DATA ANALYSIS, 44(4), PII S0167-9473(02)00263-3. p. 649-667
Abstract: A pairwise likelihood (PL) estimation procedure is examined in multilevel models with binary responses and probit link. The PL is obtained as the product of bivariate likelihoods for within-cluster pairs of observations. The resulting estimator still enjoys desirable asymptotic properties such as consistency and asymptotic normality. Therefore, with this approach a compromise between computational burden and loss of efficiency is sought. A simulation study was conducted to compare PL with second-order penalized quasi-likelihood (PQL2) and maximum (marginal) likelihood (ML) estimation methods. The loss of efficiency of the PL estimator is found to be generally moderate. Also, PL tends to show more robustness against convergence problems than PQL2. (C) 2002 Elsevier B.V.. All rights reserved.
URI: http://hdl.handle.net/1942/2206
DOI: 10.1016/S0167-9473(02)00263-3
ISI #: 000187752100008
ISSN: 0167-9473
Category: A1
Type: Journal Contribution
Validation: ecoom, 2005
Appears in Collections: Research publications

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