Nonparametric tolerance intervals for effective bootstrap estimation

被引：0
|
作者：
Sarma, A [1 ]
Tufts, DW [1 ]
机构：
[1] USN, Undersea Warfare Ctr, Newport, RI 02841 USA
来源：
THIRTY-SIXTH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS - CONFERENCE RECORD, VOLS 1 AND 2, CONFERENCE RECORD | 2002年
关键词：
distribution-free method; Monte Carlo approximation; quantile and parameter estimation;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A method that allows accurate control of the coverage error in Monte Carlo-approximation of quintiles of the bootstrap distribution is discussed. The method is based on Nonparametric Tolerance Intervals and hence is applicable regardless of the underlying distribution. The results are useful for quantile estimation as well as for construction of robust confidence intervals and interval estimates. The minimum number of bootstrap replicates needed to estimate quantiles to a prescribed conditional coverage accuracy is determined. The results allow the user to perform bootstrap inference without being subject to intolerable fluctuations from Monte Carlo error.
引用
收藏
页码：1757 / 1761
页数：5
相关论文
共 50 条
12345