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Bootstrapping for highly unbalanced clustered data

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  • Samanta, Mayukh
  • Welsh, A.H.

Abstract

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.

Suggested Citation

  • Samanta, Mayukh & Welsh, A.H., 2013. "Bootstrapping for highly unbalanced clustered data," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 70-81.
    • Handle: RePEc:eee:csdana:v:59:y:2013:i:c:p:70-81
    DOI: 10.1016/j.csda.2012.09.004
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    References listed on IDEAS

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    1. Matías Salibián-Barrera & Stefan Aelst & Gert Willems, 2008. "Fast and robust bootstrap," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 17(1), pages 41-71, February.
    2. Field, C. A. & Pang, Zhen & Welsh, A. H., 2010. "Bootstrapping Robust Estimates for Clustered Data," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1606-1616.
    3. C. A. Field & A. H. Welsh, 2007. "Bootstrapping clustered data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(3), pages 369-390, June.
    4. Salibian-Barrera, Matias & Van Aelst, Stefan, 2008. "Robust model selection using fast and robust bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5121-5135, August.
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    Cited by:

    1. Salibian-Barrera, Matias & Van Aelst, Stefan & Yohai, Víctor J., 2016. "Robust tests for linear regression models based on τ-estimates," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 436-455.
    2. J. L. Scealy & A. H. Welsh, 2017. "A Directional Mixed Effects Model for Compositional Expenditure Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 24-36, January.
    3. Janice L. Scealy & David Heslop & Jia Liu & Andrew T. A. Wood, 2022. "Directions Old and New: Palaeomagnetism and Fisher (1953) Meet Modern Statistics," International Statistical Review, International Statistical Institute, vol. 90(2), pages 237-258, August.
    4. O’Shaughnessy, P.Y. & Welsh, A.H., 2018. "Bootstrapping longitudinal data with multiple levels of variation," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 117-131.

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