Bootstrapping for highly unbalanced clustered data

Mayukh Samanta*, A. H. Welsh

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    10 Citations (Scopus)


    We apply the generalized cluster bootstrap to both Gaussian quasi-likelihood and robust estimates in the context of highly unbalanced clustered data. We compare it with the transformation bootstrap where the data are generated by the random effect and transformation models and all the random variables have different distributions. We also develop a fast approach (proposed by Salibian-Barrera et al. (2008)) and show that it produces some encouraging results. We show that the generalized bootstrap performs better than the transformation bootstrap for highly unbalanced clustered data. We apply the generalized cluster bootstrap to a sample of income data for Australian workers.

    Original languageEnglish
    Pages (from-to)70-81
    Number of pages12
    JournalComputational Statistics and Data Analysis
    Issue number1
    Publication statusPublished - Mar 2013


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