Better Bootstrap Confidence Intervals

Abstract

We consider the problem of setting approximate confidence intervals for a single parameter theta in a multiparameter family. The standard approximate intervals based on maximum likelihood theory, can be quite misleading so, in practice, tricks based on transformations, bias, corrections, etc., are often used to improve their accuracy. The bootstrap confidence intervals discussed in this paper automatically incorporate such tricks without requiring the statistician to think them through for each new application, at the price of a considerable increase in computational effort. In addition to parametric families, bootstrap intervals are also developed for nonparametric situations. Originator-supplied keywords include: nonparametric intervals and Secondary order theory.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1984
Accession Number
ADA150798

Entities

People

  • B. Efron

Organizations

  • Stanford University

Tags

DTIC Thesaurus Topics

  • Accuracy
  • Intervals

Fields of Study

  • Mathematics

Readers

  • Statistical inference.
  • Systems Analysis and Design