Nonparametric Estimation of the Generalized Variance.

Abstract

For multivariate distributions with finite second order moments, a nonparametric symmetric, unbiased estimator of the generalized variance is considered, and it is shown to be (nonparametric) optimal for the class of distributions having finite fourth order moments. A jackknifed version of the sample generalized variance is also considered as a contender; it is computationally more convenient and asymptotically equivalent to the former. It is also shown that the second estimator performs quite well (in large sample) relative to the optimal normal theory estimators under several loss functions. (Keywords: kernels; U-statistics; von mises' functionals).

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1986
Accession Number
ADA186029

Entities

People

  • Bimal K. Sinha
  • Pranab K. Sen

Organizations

  • University of Pittsburgh

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Air Force
  • Computations
  • Contracts
  • Distribution Functions
  • Estimators
  • Governments
  • Maryland
  • Military Research
  • Multivariate Analysis
  • National Governments
  • North Carolina
  • Scientific Research
  • Statistics
  • United States
  • United States Government
  • Universities

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

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