Original paper
A Nondegenerate Penalized Likelihood Estimator for Variance Parameters in Multilevel Models
Abstract
Group-level variance estimates of zero often arise when fitting multilevel or hierarchical linear models, especially when the number of groups is small. For situations where zero variances are implausible a priori, we propose a maximum penalized likelihood approach to avoid such boundary estimates. This approach is equivalent to estimating variance parameters by their posterior mode, given a weakly informative prior distribution. By choosing the...
Paper Details
Title
A Nondegenerate Penalized Likelihood Estimator for Variance Parameters in Multilevel Models
Published Date
Mar 12, 2013
Journal
Volume
78
Issue
4
Pages
685 - 709
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Notes
History