Recursive Parameter Estimation Using Incomplete Data.

Abstract

Stochastic approximation procedures are considered for the estimation of parameters using incomplete data. One procedure is stated and illustrated which often leads to asymptotically efficient estimators. Others are developed which, although possibly not optimal in the above sense, will be very much easier to apply. This will be particularly advantageous when quick recursive estimates are required. Examples are given and a link is made between one of the sub-optimal methods and the EM algorithm. (Author)

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1982
Accession Number
ADA116190

Entities

People

  • D. M. Titterington

Organizations

  • University of Wisconsin–Madison

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Algorithms
  • Asymptotic Normality
  • Computations
  • Data Analysis
  • Data Science
  • Estimators
  • Information Science
  • Mathematics
  • Maximum Likelihood Estimation
  • North Carolina
  • Probability
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics
  • United States
  • Universities
  • Wisconsin

Fields of Study

  • Mathematics

Readers

  • Statistical inference.