A Bias Bound for Least Squares Linear Regression

Abstract

Least squares linear regression is one of the most widely statistical tools. It is based on a certain standard linear model where y denotes a scalar outcome variable, and x denotes a p-dimensional column vector of regressor variables. In empirical applications, it is unlikely for the standard linear model to hold exactly. Therefore we need to be concerned about possible violations of the model assumptions. For example, we might consider distribution violation: the error distribution might not be normal. There is a rich literature on robust methods for estimating the linear model in the presence of distribution violation.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1991
Accession Number
ADA256891

Entities

People

  • Ker-chau Li
  • Naihua Duan

Organizations

  • RAND Corporation

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Algebra
  • California
  • Construction
  • Corporations
  • Curvature
  • Data Science
  • Decomposition
  • Eigenvalues
  • Eigenvectors
  • Environmental Protection
  • Geometry
  • Information Science
  • Mathematics
  • New York
  • Standards
  • Statistical Analysis
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Graph Algorithms and Convex Optimization.
  • Regression Analysis.