ASYMPTOTIC MINIMAX AND ADMISSIBILITY IN ESTIMATION.

Abstract

A sequence of general experiments is considered over a k-dimensional parameter. Under conditions of local asymptotic normality (LAN) of the families of distributions, we prove that, from the point of view of the local asymptotic minimax, there is a lower bound, which may be obtained only if the estimator has certain linear relation to the derivative of the likelihood function. This entails asymptotic normality with Fisher's variance. Conditions LAN are proved under the sole condition of continuity of Fisher's information. (Author)

Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1970
Accession Number
AD0712032

Entities

People

  • Jaroslav Hajek

Organizations

  • Florida State University

Tags

DTIC Thesaurus Topics

  • Asymptotic Normality
  • Computing-Related Activities
  • Continuity
  • Data Science
  • Estimators
  • Information Science
  • Interdisciplinary Science
  • Mathematics
  • Normality
  • Sequences
  • Statistical Algorithms
  • Statistical Analysis

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Mathematical Modeling and Probability Theory.