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An empirical study of statistical properties of variance partition coefficients for multi-level logistic regression models

Communications in Statistics: Simulation and Computation

By:
, , and
DOI: 10.1080/03610910802361366

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Abstract

Partitioning the variance of a response by design levels is challenging for binomial and other discrete outcomes. Goldstein (2003) proposed four definitions for variance partitioning coefficients (VPC) under a two-level logistic regression model. In this study, we explicitly derived formulae for multi-level logistic regression model and subsequently studied the distributional properties of the calculated VPCs. Using simulations and a vegetation dataset, we demonstrated associations between different VPC definitions, the importance of methods for estimating VPCs (by comparing VPC obtained using Laplace and penalized quasilikehood methods), and bivariate dependence between VPCs calculated at different levels. Such an empirical study lends an immediate support to wider applications of VPC in scientific data analysis.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
An empirical study of statistical properties of variance partition coefficients for multi-level logistic regression models
Series title:
Communications in Statistics: Simulation and Computation
DOI:
10.1080/03610910802361366
Volume
37
Issue:
10
Year Published:
2008
Language:
English
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Communications in Statistics: Simulation and Computation
First page:
2010
Last page:
2026
Number of Pages:
17