On Jointly Estimating Parameters and Missing Data by Maximizing the Complete-Data Likelihood.

Abstract

One approach to handling incomplete data occasionally encountered in the literature is to treat the missing data as parameters and to maximize the complete data likelihood over missing data and parameters. This paper points out that although this approach can be useful in particular problems, it is not a generally reliable approach to the analysis of incomplete data. In particular, it does not share the optimal properties of maximum likelihood estimation, except under the trivial asymptotics in which the proportion of missing data goes to zero as the sample size increases. (Author)

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1982
Accession Number
ADA114533

Entities

People

  • Donald B. Rubin
  • Roderick J. A. Little

Organizations

  • University of Wisconsin–Madison

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Analysis Of Variance
  • Classification
  • Data Science
  • Experimental Design
  • Information Science
  • Literature
  • Mathematical Analysis
  • Mathematics
  • Maximum Likelihood Estimation
  • Military Research
  • Normal Distribution
  • Probability
  • Probability Distributions
  • Random Variables
  • Statistical Algorithms
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.
  • Statistical inference.