New Methods for Estimating Tail Probabilities and Extreme Value Disributions.

Abstract

This research has focused on the problem of estimating probabilities in the upper tail of an underlying distribution and the corresponding quantiles based on a random sample from the distribution. Two estimation procedures, exponential tail and transformed exponential tail, were defined and their bias and variance properties were thoroughly studied both analytically and by means of an extensive Monte Carlo experiment. The experiment involved several forms of each of the two procedures; twenty underlying distributions were simulated, including a variety of Weibull and lognormal distributions; four sample sizes were considered--100, 200, 400 and 800. Careful study of the analytic and Monte Carlo results showed that exponential tail and transformed exponential tail procedures worked quite well, but indicated a potential for substantial further improvement by properly combining them. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Dec 14, 1979
Accession Number
ADA080446

Entities

People

  • Charles J. Stone
  • John D. Gins
  • Leo Breiman

Organizations

  • Technology Service Corporation

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Computations
  • Convergence
  • Data Science
  • Distribution Functions
  • Efficiency
  • Equations
  • Errors
  • Estimators
  • Information Science
  • Normal Distribution
  • Numerical Analysis
  • Probability
  • Random Variables
  • Standards
  • Statistical Samples
  • Statistics
  • Theorems

Fields of Study

  • Mathematics

Readers

  • Statistical inference.