Jackknifing under a Budget Constraint

Abstract

This paper considers the problem of estimating a parameter alpha that can be expressed as a nonlinear function of samples means. We develop a jackknife estimator for alpha that is appropriate to computational settings in which the total computer budget to be used is constrained. Despite the fact that the jackknifed observations are not i.i.d., we are able to show that our jackknife estimator reduces bias without increasing asymptotic variance. This makes the estimator particularly suitable for small sample applications. Because a special case of this estimator problem is that of estimating a ratio of two means, the results in this paper are pertinent to regenerative steady-state simulations.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1989
Accession Number
ADA220222

Entities

People

  • Peter W. Glynn
  • Philip Heidelberger

Organizations

  • Stanford University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Computers
  • Contracts
  • Engineering
  • Markov Processes
  • Mathematics
  • Military Research
  • Operations Research
  • Probability
  • Random Variables
  • Simulations
  • Standards
  • Statistical Algorithms
  • Statistics
  • Steady State
  • Stochastic Processes
  • Systems Engineering
  • Theorems

Fields of Study

  • Mathematics

Readers

  • Statistical inference.