Restricted L1 Estimators and Their Covariances,

Abstract

The parameters in a linear regression model can be estimated by minimizing the sum of the absolute residuals (L1 estimation) instead of the more classical approach of minimizing the sum of squared residuals (least squares estimation). In addition to other nice properties L1 estimators are less sensitive to outliers than least squares estimators. This paper describes a linear programming algorithm and computer program for obtaining L1 estimators and estimates of their covariances when the regression parameters are restricted to satisfy specified linear constraints. These estimated covariances are the new feature in this work and are an extremely important ingredient in hypothesis tests and confidence interval construction.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1980
Accession Number
ADA113387

Entities

People

  • D. Book
  • Herman Otto Hartley
  • J. Booker
  • R. L. Sielken Jr.

Organizations

  • Texas A&M University

Tags

Communities of Interest

  • C4I
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Applied Mathematics
  • Business Administration
  • Computations
  • Computer Programming
  • Computer Programs
  • Computers
  • Costs
  • Estimators
  • Information Science
  • Linear Programming
  • Military Research
  • Operations Research
  • Plastic Explosives
  • Random Variables
  • Statistics
  • Two Dimensional

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Regression Analysis.