Document Server@UHasselt >
Research >
Research publications >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/16505

Title: A Taxonomy of Mixing and Outcome Distributions Based on Conjugacy and Bridging
Authors: Kenward, Michael G.
Molenberghs, Geert
Issue Date: 2016
Abstract: The generalized linear mixed model is commonly used for the analysis of hierarchical non-Gaussian data. It combines an exponential family model formulation with normally distributed random effects. A drawback is the difficulty of deriving convenient marginal mean functions with straightforward parametric interpretations. Several solutions have been proposed, including the marginalized multilevel model (directly formulating the marginal mean, together with a hierarchical association structure) and the bridging approach (choosing the random-effects distribution such that marginal and hierarchical mean functions share functional forms). Another approach, useful in both a Bayesian and a maximum likelihood setting, is to choose a random-effects distribution that is conjugate to the outcome distribution. In this paper, we contrast the bridging and conjugate approaches. For binary outcomes, using characteristic functions and cumulant generating functions, it is shown that the bridge distribution is unique. Self-bridging is introduced as the situation in which the outcome and random-effects distributions are the same. It is shown that only the Gaussian and degenerate distributions have well-defined cumulant generating functions for which self-bridging holds.
Notes: Molenberghs, G (reprint author), Univ Hasselt, I BioStat, Martelarenlaan 42, B-3500 Hasselt, Belgium. geert.molenberghs@uhasselt.be
URI: http://hdl.handle.net/1942/16505
DOI: 10.1080/03610926.2013.870205
ISI #: 000372828900009
ISSN: 0361-0926
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

Files in This Item:

Description SizeFormat
Peer-reviewed author version181.66 kBAdobe PDF
Published version684.26 kBAdobe PDF

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.