Improved Estimation of the Disturbance Variance in a Linear Regression Model

Abstract

This paper considers estimation of the disturbance variance in a linear regression model. A new class of improved estimators is obtained by extending results dating to Stein (1964). These estimators dominate the ordinary least square estimator under squared error loss.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jul 11, 1989
Accession Number
ADA210272

Entities

People

  • Alan E. Gelfand
  • Dipak K. Dey

Organizations

  • Stanford University

Tags

DTIC Thesaurus Topics

  • California
  • Computations
  • Contracts
  • Covariance
  • Data Science
  • Estimators
  • Governments
  • Information Science
  • Military Research
  • Permutations
  • Random Variables
  • Sequences
  • Statistics
  • United States
  • United States Government
  • Vector Spaces

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.