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A note on the coverage behaviour of bootstrap percentile confidence intervals for constrained parameters

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Listed:
  • Chunlin Wang

    (Xiamen University)

  • Paul Marriott

    (University of Waterloo)

  • Pengfei Li

    (University of Waterloo)

Abstract

The asymptotic behaviour of the commonly used bootstrap percentile confidence interval is investigated when the parameters are subject to linear inequality constraints. We concentrate on the important one- and two-sample problems with data generated from general distributions in the natural exponential family. The focus of this note is on quantifying the coverage probabilities of the parametric bootstrap percentile confidence intervals, in particular their limiting behaviour near boundaries. We propose using a local asymptotic framework to study this subtle coverage behaviour. Under this framework, we discover that when the true parameters are on, or close to, the restriction boundary, the asymptotic coverage probabilities can always exceed the nominal level in the one-sample case; however, they can be, remarkably, both under and over the nominal level in the two-sample case. Using illustrative examples, we show that the results provide theoretical justification and guidance on applying the bootstrap percentile method to constrained inference problems.

Suggested Citation

  • Chunlin Wang & Paul Marriott & Pengfei Li, 2022. "A note on the coverage behaviour of bootstrap percentile confidence intervals for constrained parameters," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(7), pages 809-831, October.
    • Handle: RePEc:spr:metrik:v:85:y:2022:i:7:d:10.1007_s00184-021-00851-0
    DOI: 10.1007/s00184-021-00851-0
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    References listed on IDEAS

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