Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1147665 | Journal of Statistical Planning and Inference | 2011 | 12 Pages |
Abstract
A question of fundamental importance for meta-analysis of heterogeneous multidimensional data studies is how to form a best consensus estimator of common parameters, and what uncertainty to attach to the estimate. This issue is addressed for a class of unbalanced linear designs which include classical growth curve models. The solution obtained is similar to the popular DerSimonian and Laird (1986) method for a simple meta-analysis model. By using almost unbiased variance estimators, an estimator of the covariance matrix of this procedure is derived. Combination of these methods is illustrated by two examples and are compared via simulation.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Andrew L. Rukhin,