Percentile and Percentile-t Bootstrap Confidence Intervals: A Practical Comparison

2015 ◽  
Vol 4 (1) ◽  
Author(s):  
Christopher J. Elias
Keyword(s):  
Monte Carlo ◽  
Confidence Intervals ◽  
Monte Carlo Study ◽  
Bootstrap Confidence Intervals ◽  
Bootstrap Percentile

AbstractThis paper employs a Monte Carlo study to compare the performance of equal-tailed bootstrap percentile-

A note on coverage error of bootstrap confidence intervals for quantiles

1993 ◽  
Vol 114 (3) ◽  
pp. 517-531 ◽  
Author(s):  
D. De Angelis ◽  
Peter Hall ◽  
G. A. Young
Keyword(s):  
Confidence Intervals ◽  
Present Note ◽  
Normal Approximation ◽  
Poor Performance ◽  
Density Estimator ◽  
Bootstrap Confidence Intervals ◽  
Coverage Error ◽  
Bootstrap Percentile ◽  
Coverage Accuracy ◽  
Percentile Bootstrap

AbstractAn interesting recent paper by Falk and Kaufmann[11] notes, with an element of surprise, that the percentile bootstrap applied to construct confidence intervals for quantiles produces two-sided intervals with coverage error of size n−½, where n denotes sample size. By way of contrast, the error would be O(n−1) for two-sided intervals in more classical problems, such as intervals for means or variances. In the present note we point out that the relatively poor performance in the case of quantiles is shared by a variety of related procedures. The coverage accuracy of two-sided bootstrap intervals may be improved to o(n−½) by smoothing the bootstrap. We show too that a normal approximation method, not involving the bootstrap but incorporating a density estimator as part of scale estimation, can have coverage error O(n−1+∈), for arbitrarily small ∈ > 0. Smoothed and unsmoothed versions of bootstrap percentile-t are also analysed.

scholarly journals Comparison of Bootstrap Confidence Intervals Using Monte Carlo Simulations

Water ◽  
2018 ◽  
Vol 10 (2) ◽  
pp. 166 ◽  
Author(s):  
Roberto Flowers-Cano ◽  
Ruperto Ortiz-Gómez ◽  
Jesús León-Jiménez ◽  
Raúl López Rivera ◽  
Luis Perera Cruz
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Confidence Intervals ◽  
Bootstrap Confidence Intervals

scholarly journals Estimators and Bootstrap Confidence Intervals for Ruin Probabilities

1989 ◽  
Vol 19 (1) ◽  
pp. 57-70 ◽  
Author(s):  
Christian Hipp
Keyword(s):  
Confidence Intervals ◽  
Bootstrap Method ◽  
Monte Carlo Study ◽  
Risk Process ◽  
Claim Amount ◽  
Ruin Probabilities ◽  
Infinite Time ◽  
Bootstrap Confidence Intervals ◽  
Classical Risk Process ◽  
The Bootstrap Method

AbstractFor the infinite time ruin probability in the classical risk process, efficient estimators are proposed in cases in which the claim amount distribution is unknown. Confidence intervals are computed which are based on normal approximations or on the bootstrap method. The procedures are checked in a Monte-Carlo study.

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