An empirical study of statistical properties of variance partition coefficients for multi-level logistic regression models
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.
Citation Information
Publication Year | 2008 |
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Title | An empirical study of statistical properties of variance partition coefficients for multi-level logistic regression models |
DOI | 10.1080/03610910802361366 |
Authors | Ji Li, B. R. Gray, D.M. Bates |
Publication Type | Article |
Publication Subtype | Journal Article |
Series Title | Communications in Statistics: Simulation and Computation |
Index ID | 70033666 |
Record Source | USGS Publications Warehouse |