The Asymptotic Validity of Sequential Stopping Rules for Stochastic Simulations

Abstract

We establish general conditions for the asymptotic validity of sequential stopping rules to achieve fixed-volume confidence sets for simulation estimators of vector-valued parameters. The asymptotic validity occurs as the prescribed volume of the confidences set approaches zero. There are two requirements: a functional central limit theorem for the estimation process and strong consistency (with-probability-one convergence) for the variance or scaling matrix estimator. Applications are given for: sample means of i.i.d. random variables and random vectors, nonlinear functions of such sample means, jackknifing, Kiefer-Wolfowitz and Robbins-Monro stochastic approximation, and both regenerative and non-regenerative steady-state simulation. Keywords: Stochastic simulation, Variance estimators.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1990
Accession Number
ADA228896

Entities

People

  • Peter W. Glynn
  • Ward Whitt

Organizations

  • Stanford University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Brownian Motion
  • Classification
  • Consistency
  • Convergence
  • Engineering
  • Estimators
  • Information Science
  • Intervals
  • Military Research
  • New York
  • Probability
  • Random Variables
  • Sequences
  • Simulations
  • Steady State
  • Stochastic Processes

Fields of Study

  • Mathematics

Readers

  • Statistical inference.