How Many Bootstraps?

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: Tables(data).

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Aug 22, 1985
Accession Number
ADA160043

Entities

People

  • R. Tibshirani

Organizations

  • Stanford University

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Accuracy
  • Coefficients
  • Data Science
  • Distribution Functions
  • Errors
  • Information Science
  • Intervals
  • Military Research
  • Monte Carlo Method
  • New York
  • Normal Distribution
  • North Carolina
  • Sampling
  • Standards
  • Statistical Analysis
  • Statistics
  • United States

Fields of Study

  • Mathematics

Readers

  • Statistical inference.