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The effect of Monte Carlo approximation on coverage error of double‐bootstrap confidence intervals

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  • S. M. S. Lee
  • G. A. Young

Abstract

A double‐bootstrap confidence interval must usually be approximated by a Monte Carlo simulation, consisting of two nested levels of bootstrap sampling. We provide an analysis of the coverage accuracy of the interval which takes account of both the inherent bootstrap and Monte Carlo errors. The analysis shows that, by a suitable choice of the number of resamples drawn at the inner level of bootstrap sampling, we can reduce the order of coverage error. We consider also the effects of performing a finite Monte Carlo simulation on the mean length and variability of length of two‐sided intervals. An adaptive procedure is presented for the choice of the number of inner level resamples. The effectiveness of the procedure is illustrated through a small simulation study.

Suggested Citation

  • S. M. S. Lee & G. A. Young, 1999. "The effect of Monte Carlo approximation on coverage error of double‐bootstrap confidence intervals," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 353-366, April.
  • Handle: RePEc:bla:jorssb:v:61:y:1999:i:2:p:353-366
    DOI: 10.1111/1467-9868.00181
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    Cited by:

    1. G. Alastair Young, 2003. "Better bootstrapping by constrained prepivoting," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(2), pages 227-242.

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