Bootstrap diagnostics and remedies

被引:27
作者
Canty, AJ
Davison, AC
Hinkley, DV
Ventura, V
机构
[1] McMaster Univ, Dept Math & Stat, Hamilton, ON L8S 4K1, Canada
[2] Ecole Polytech Fed Lausanne, FSB IMA STAT, Inst Math, Stn 8, CH-1015 Lausanne, Switzerland
[3] Univ Calif Santa Barbara, Dept Stat & Appl Probabil, Santa Barbara, CA 93106 USA
[4] Carnegie Mellon Univ, Dept Stat, Pittsburgh, PA 15213 USA
来源
CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE | 2006年 / 34卷 / 01期
关键词
bootstrap recycling; importance sampling; inconsistency; jackknife-after-bootstrap; outlier; pivot; resampling; spatial data; Stein estimator; subsampling; superefficiency; time series;
D O I
10.1002/cjs.5550340103
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Bootstrap diagnostics are used to assess the reliability of bootstrap calculations and may suggest useful modified calculations when these are possible. Concern focuses on susceptibility to peculiarities in data, incorrectness of a resampling model, incorrect use of resampling simulation output, and inherent inaccuracy of the bootstrap approach. The last involves issues such as inconsistency of a bootstrap method, the order of correctness of a consistent bootstrap method, and approximate pivotality. The authors review here some of these problems, provide workable diagnostic methods where possible, and discuss fast and simple ways to effect the necessary computations.
引用
收藏
页码:5 / 27
页数:23
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