CONSTRAINED MAXIMUM LIKELIHOOD ESTIMATION OF N STOCHASTICALLY ORDERED DISTRIBUTIONS

Abstract

The study considers the problem of determining step function maximum likelihood estimates for N stochastically ordered distributions, subject to the constraint that the estimates themselves must also be stochastically ordered. This problem arises, for example, in the context of reliability growth. Brunk, et al., has achieved a closed form solution for the case N = 2, but was unable to extend the results to the case N > 2. We shall present a new analytical method based on the Kuhn-Tucker optimality conditions for the equivalent concave program. For N = 2, the method yields the closed form solution of Brunk, and for N = or > 3, the method yields an efficient computational algorithm. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1968
Accession Number
AD0673232

Entities

People

  • Arthur M. Geoffrion

Organizations

  • University of California, Los Angeles

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Air Force
  • Algorithms
  • Classification
  • Computational Science
  • Computer Programming
  • Construction
  • Discrete Distribution
  • Distribution Functions
  • Errors
  • Instructions
  • Intervals
  • Maximum Likelihood Estimation
  • Operations Research
  • Probability
  • Security

Fields of Study

  • Mathematics

Readers

  • Operations Research
  • Statistical inference.