Ridge Estimation for the Linear Regression Model.

Abstract

A class of estimators, variously known as ridge estimators, is considered for the linear regression model Y=X theta + epsilon, where theta is an unknown parameter vector to be estimated. Some properties of the ridge estimators are given. It is shown that certain ridge estimators have uniformly smaller mean squared error than the least squares estimator. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Feb 18, 1977
Accession Number
ADA082178

Entities

People

  • James S. Hawkes
  • Khursheed Alam

Organizations

  • Clemson University

Tags

DTIC Thesaurus Topics

  • Bayesian Networks
  • Classification
  • Computational Science
  • Contracts
  • Data Science
  • Estimators
  • Hypergeometric Functions
  • Information Science
  • Least Squares Method
  • Military Research
  • Models
  • Normal Distribution
  • Random Variables
  • Simulations
  • Statistical Algorithms
  • Test And Evaluation

Fields of Study

  • Mathematics

Readers

  • Analytical Mechanics
  • Regression Analysis.