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Resampling methods for estimating functions with U-statistic structure

Author

Listed:
  • Wenyu Jiang

    (University of Waterloo)

  • Jack Kalbfleisch

    (University of Michigan)

Abstract

Suppose that inference about parameters of interest is to be based on an unbiased estimating function that is U-statistic of degree 1 or 2. We define suitable studentized versions of such estimating functions and consider asymptotic approximations as well as an estimating function bootstrap (EFB) method based on resampling the estimated terms in the estimating functions. These methods are justified asymptotically and lead to confidence intervals produced directly from the studentized estimating functions. Particular examples in this class of estimating functions arise in La estimation as well as Wilcoxon rank regression and other related estimation problems. The proposed methods are evaluated in examples and simulations and compared with a recent suggestion for inference in such problems which relies on resampling an underlying objective functions with U-statistic structure.

Suggested Citation

  • Wenyu Jiang & Jack Kalbfleisch, 2004. "Resampling methods for estimating functions with U-statistic structure," The University of Michigan Department of Biostatistics Working Paper Series 1032, Berkeley Electronic Press.
    • Handle: RePEc:bep:mchbio:1032
    Note: oai:bepress.com:umichbiostat-1032
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