Title :
Nonparametric tolerance intervals for effective bootstrap estimation
Author :
Sarma, A. ; Tufts, D.W.
Author_Institution :
Naval Underwater Syst. Center, Newport, RI, USA
Abstract :
A method that allows accurate control of the coverage error in Monte Carlo approximation of quantiles of the bootstrap distribution is discussed. The method is based on nonparametric tolerance internonparametric 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.
Keywords :
Monte Carlo methods; approximation theory; bootstrapping; filtering theory; matched filters; signal processing; statistical analysis; Monte Carlo approximation; Monte Carlo error; bootstrap distribution; bootstrap estimation; bootstrap inference; bootstrap replicates; bootstrapped matched filter; conditional coverage accuracy; coverage error control; nonparametric tolerance intervals; quantile estimation; robust confidence intervals; signal processing; Error correction codes; Gaussian noise; Matched filters; Monte Carlo methods; Parameter estimation; Random variables; Statistical analysis; Statistical distributions; Testing; Tiles;
Conference_Titel :
Signals, Systems and Computers, 2002. Conference Record of the Thirty-Sixth Asilomar Conference on
Conference_Location :
Pacific Grove, CA, USA
Print_ISBN :
0-7803-7576-9
DOI :
10.1109/ACSSC.2002.1197076
