IMPROVED ESTIMATION OF REGRESSION PARAMETERS.

Abstract

Correspondences between the problems of estimating the mean of a multivariate normal distribution and estimating regression parameters are presented and investigated to obtain minimax or admissible estimators of the regression parameters in normal multivariate (and univariate) regression models with respect to squared-distance loss functions. These new estimators are better than the maximum likelihood estimator, in that their risks are smaller, for all parameter values. (Author)

Document Details

Document Type
Technical Report
Publication Date
Jan 25, 1967
Accession Number
AD0649679

Entities

People

  • Stanley L. Sclove

Organizations

  • Stanford University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Distribution Functions
  • Estimators
  • Functions (Mathematics)
  • Mathematics
  • Normal Distribution

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.

Technology Areas

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