Within-cluster resampling for multilevel models under informative cluster size
| Autor: | D Lee, Chris J. Skinner, Jae Kwang Kim |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
Covariance matrix Applied Mathematics General Mathematics Multilevel model Estimator 01 natural sciences Agricultural and Biological Sciences (miscellaneous) 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Consistency (statistics) Resampling Statistics Linear regression Cluster size Cluster (physics) 030212 general & internal medicine QA Mathematics 0101 mathematics Statistics Probability and Uncertainty General Agricultural and Biological Sciences Mathematics |
| Popis: | Summary A within-cluster resampling method is proposed for fitting a multilevel model in the presence of informative cluster size. Our method is based on the idea of removing the information in the cluster sizes by drawing bootstrap samples which contain a fixed number of observations from each cluster. We then estimate the parameters by maximizing an average, over the bootstrap samples, of a suitable composite loglikelihood. The consistency of the proposed estimator is shown and does not require that the correct model for cluster size is specified. We give an estimator of the covariance matrix of the proposed estimator, and a test for the noninformativeness of the cluster sizes. A simulation study shows, as in Neuhaus & McCulloch (2011), that the standard maximum likelihood estimator exhibits little bias for some regression coefficients. However, for those parameters which exhibit nonnegligible bias, the proposed method is successful in correcting for this bias. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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