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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/14593

Title: A hierarchical Bayesian approach for the analysis of longitudinal count data with overdispersion: A simulation study
Authors: Aregay, Mehreteab
Shkedy, Ziv
Molenberghs, Geert
Issue Date: 2013
Publisher: ELSEVIER SCIENCE BV
Citation: COMPUTATIONAL STATISTICS & DATA ANALYSIS, 57 (1), p. 233-245
Abstract: In sets of count data, the sample variance is often considerably larger or smaller than the sample mean, known as a problem of over- or underdispersion. The focus is on hierarchical Bayesian modeling of such longitudinal count data. Two different models are considered. The first one assumes a Poisson distribution for the count data and includes a subject-specific intercept, which is assumed to follow a normal distribution, to account for subject heterogeneity. However, such a model does not fully address the potential problem of extra-Poisson dispersion. The second model, therefore, includes also random subject and time dependent parameters, assumed to be gamma distributed for reasons of conjugacy. To compare the performance of the two models, a simulation study is conducted in which the mean squared error, relative bias, and variance of the posterior means are compared. (C) 2012 Elsevier B.V. All rights reserved.
Notes: [Shkedy, Ziv; Molenberghs, Geert] Univ Hasselt, I BioStat, B-3590 Diepenbeek, Belgium. [Aregay, Mehreteab; Molenberghs, Geert] Katholieke Univ Leuven, I BioStat, Louvain, Belgium. geert.molenberghs@uhasselt.be
URI: http://hdl.handle.net/1942/14593
DOI: 10.1016/j.csda.2012.06.020
ISI #: 000310403700017
ISSN: 0167-9473
Category: A1
Type: Journal Contribution
Validation: ecoom, 2013
Appears in Collections: Research publications

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