On the Asymptotics of Maximum Likelihood and Related Estimators Based on Type II Censored Data.

Abstract

Some simple procedures are provided for establishing the asymptotic normality and uniform strong convergence of a class of functions that arise in the context of estimating parameters from a type II censored sample. These are used to streamline and strengthen the traditional treatment of the asymptotic theory of maximum likelihood estimators based on censored data. Further applications include the treatment of asymptotics of some modified maximum likelihood (MML) estimators. In particular, conditions are provided for the consistency and limiting normality of the MML estimators of Mehrotra and Nanda, and the asymptotic efficiencies of these estimators are evaluated. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1983
Accession Number
ADA137958

Entities

People

  • G. K. Bhattacharyya

Organizations

  • University of Wisconsin–Madison

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Asymptotic Normality
  • Consistency
  • Convergence
  • Data Science
  • Efficiency
  • Estimators
  • Information Science
  • Mathematics
  • Military Research
  • Normal Distribution
  • Normality
  • Order Statistics
  • Probability
  • Random Variables
  • Statistical Algorithms
  • Statistics
  • United States

Fields of Study

  • Mathematics

Readers

  • Statistical inference.