Approximate Tail Probabilities for the Maxima of Some Random Fields

Abstract

Hogan and Siegmund (1986) adapt the method developed by Pickands (1969), Qualls and Watanabe (1973), and Bickel and Rosenblatt (1973) to obtain explicit large deviation approximations for the maxima of several Gaussian random fields arising in statistics. Using a special argument for one particular case, they suggest a heuristic second order approximation for that case; and they show by a Monte Carlo experiment that the second order approximation frequently gives considerably better numerical results. The purpose of this paper is to show that the method developed by Woodroofe (1976,1982) for problems in one dimensional time can be adapted to study maxima of random fields. Overall it involves simpler computations than the previous method and consequently seems potentially capable of delivering a genuine second order approximation should one seem desirable.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1986
Accession Number
ADA172729

Entities

People

  • David Siegmund

Organizations

  • Stanford University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Applied Mathematics
  • Data Science
  • Distribution Functions
  • Gaussian Processes
  • New York
  • Normal Distribution
  • Order Statistics
  • Probability
  • Probability Distributions
  • Random Variables
  • Random Walk
  • Sequences
  • Sequential Analysis
  • Statistics
  • Two Dimensional
  • United States
  • United States Government

Fields of Study

  • Mathematics

Readers

  • Calculus or Mathematical Analysis
  • Statistical inference.