Accession Number:

ADA160043

Title:

How Many Bootstraps?

Descriptive Note:

Technical rept.,

Corporate Author:

STANFORD UNIV CA DEPT OF STATISTICS

Personal Author(s):

Report Date:

1985-08-22

Pagination or Media Count:

16.0

Abstract:

The bootstrap is a non-parametric method for assessing statistical accuracy. In approximating bootstrap quantities by monte carlo simulation, one must decide how many bootstrap samples to generate. This document proposes an adaptive sequential method that estimates the accuracy based on the current bootstrap samples. Bootstrap sampling is continued until the estimated accuracy is high enough. In the examples given, 100 to 300 bootstraps are sufficient for standard error and bias estimation, while 1000 bootstraps may be necessary for estimating a percentile. Additional keywords Tablesdata.

Subject Categories:

  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE