RESEARCH IN STATISTICS, INFORMATION THEORY, AND ANALYSIS.

Abstract

The classical theory of asymptotically efficient estimators is the theory of maximum likelihood (m.l.) estimators. The latter theory has the following inadequacies: (1) it applies only in the so-called regular case; (2) only estimators which are asymptotically normal are considered; (3) the theory is limited principally to one dimensional parameters; (4) the theory is limited principally to the case of independent, identically distributed observations. In papers 6, by Wolfowitz and 8,13, and 16 by Wolfowitz and Weiss there is developed a theory of asymptotically efficient estimators which has none of these inadequacies, which seems to apply to every parametric problem which appears in applications, and which includes the theory of m.l. as a special case. The theory explains why the m.l. estimator works and seems to solve the problem of asymptotically efficient estimators. The paper of this group which is easiest to read is probably no. 13. (Author)

Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1968
Accession Number
AD0671389

Entities

People

  • Jacob Wolfowitz

Organizations

  • Cornell University Department of Mathematics

Tags

DTIC Thesaurus Topics

  • Computing-Related Activities
  • Data Science
  • Estimators
  • Information Science
  • Information Theory
  • Interdisciplinary Science
  • Mathematical Analysis
  • Mathematics
  • Observation
  • Statistical Analysis
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Statistical inference.
  • Theoretical Analysis.