Estimation of the Non-Centrality Parameter of a Chi-Square Distribution

Abstract

The non-central chi-square distribution arises in various statistical analyses. The estimation of the non-centrality parameter of the distribution is of importance in some problems. In this paper it is shown that the maximum likelihood estimator is inadmissible with respect to the squared error loss function. It is trivially minimax since all estimators have unbounded maximum risk. A class of estimators is given which are admissible and minimax for a modified loss function.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1980
Accession Number
ADA083527

Entities

People

  • K. M. Lal Saxena
  • Khursheed Alam

Organizations

  • Clemson University

Tags

DTIC Thesaurus Topics

  • Analysis Of Variance
  • Asymptotic Series
  • Bessel Functions
  • Chi Square Test
  • Computations
  • Data Science
  • Electrical Engineering
  • Engineering
  • Estimators
  • Hypergeometric Functions
  • Inequalities
  • Information Science
  • Mathematics
  • Military Research
  • Multivariate Analysis
  • South Carolina
  • Statistical Analysis

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms