ITERATIVE MAXIMUM-LIKELIHOOD ESTIMATION OF THE PARAMETERS OF NORMAL POPULATIONS FROM SINGLY AND DOUBLY CENSORED SAMPLES

Abstract

Iterative procedures are given for joint maximum-likelihood estimation based on singly and doubly censored samples from a normal population. The simultaneous equations yielding the maximum-likelihood estimates are obtained. Since their algebraic solution is impossible, iterative procedures are proposed which are applicable in the most general case in which both parameters are unknown and in special cases in which either of the parameters is known. The asymptotic variances and covariances are tabulated for 10% censoring intervals. A Monte Carlo investigation of the means and standard deviations of the maximum-likelihood estimators was made for 1000 samples from the standard normal population for n = 10 and n = 20. A comparison was then made of best linear unbiased estimators and maximum-likelihood estimators for n = 10 and n = 20.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1966
Accession Number
AD0647922

Entities

People

  • Albert H. Moore
  • H. L. Harter

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Air Force
  • Applied Mathematics
  • Classification
  • Contracts
  • Covariance
  • Data Science
  • Equations
  • Estimators
  • Information Science
  • Mathematics
  • Maximum Likelihood Estimation
  • Security
  • Simultaneous Equations
  • Standards
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Calculus or Mathematical Analysis
  • Statistical inference.