Bootstrapping robust estimates for clustered data

C. A. Field, Zhen Pang, A. H. Welsh

    Research output: Contribution to journalArticlepeer-review

    17 Citations (Scopus)

    Abstract

    In mixed models, the use of robust estimates raises several interesting inferential challenges. One of these challenges arises from the realization that the effect of contamination is to increase the variability in the data, but robust estimates of variance components are usually smaller than their nonrobust counterparts. The robust estimates reflect the variability of the bulk of the data, which is not the same as the variability in the data-generating process. This means that the naive implementation of bootstrap procedures might not work. In this article we consider several bootstrap procedures, including random effect, transformation, and weighted bootstraps. We give conditions for the asymptotic validity of the bootstraps and assess their performance via a small simulation study. Both the transformation and generalized cluster bootstrap perform well and are asymptotically valid under reasonable conditions.

    Original languageEnglish
    Pages (from-to)1606-1616
    Number of pages11
    JournalJournal of the American Statistical Association
    Volume105
    Issue number492
    DOIs
    Publication statusPublished - Dec 2010

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