Within-cluster resampling for multilevel models under informative cluster size

D Lee, J K Kim and C J Skinner

Biometrika, 2019, vol. 106, issue 4, 965-972

Abstract: SummaryA 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.

Keywords: Bootstrap; Composite likelihood; Generalized linear mixed model; Model misspecification; Parametric fractional imputation (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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