Estimation from Binomial Data with Classifiers of Known and Unknown Imperfections.

Abstract

Observations from inspection by a 'test' method and a standard method are combined to provide estimators of population proportion, and of probabilities of misclassification for the test method. Results of Hochberg and Tenenbein and of Albers and Veldman are extended to the case where the standard method is not perfect, but its misclassification probabilities have known values. Both moment and maximum likelihood estimators are considered and some asymptotic properties of the resulting estimators are compared. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1986
Accession Number
ADA170370

Entities

People

  • Norman L. Johnson
  • Samuel Kotz

Organizations

  • University of Maryland

Tags

Communities of Interest

  • Counter IED

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Binomials
  • Data Science
  • Estimators
  • Inspection
  • Machine Learning
  • Maryland
  • Method Of Moments
  • Military Research
  • North Carolina
  • Observation
  • Probability
  • Standards
  • Statistical Algorithms
  • Test Methods
  • Universities

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.
  • Statistical inference.