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Within-cluster resampling for multilevel models under informative cluster size

Author

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  • D Lee
  • J K Kim
  • C J Skinner

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.

Suggested Citation

  • D Lee & J K Kim & C J Skinner, 2019. "Within-cluster resampling for multilevel models under informative cluster size," Biometrika, Biometrika Trust, vol. 106(4), pages 965-972.
    • Handle: RePEc:oup:biomet:v:106:y:2019:i:4:p:965-972.
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    File URL: http://hdl.handle.net/10.1093/biomet/asz035
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    Cited by:

    1. Jae Kwang Kim & J.N.K. Rao & Yonghyun Kwon, 2022. "Analysis of clustered survey data based on two‐stage informative sampling and associated two‐level models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1522-1540, October.

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