Robust Regression Procedures for Predictor Variable Outliers.

Abstract

Least squares estimators of regression coefficients can be overly sensitive to violations of certain error assumptions; e.g., outliers in the response variable. One solution to the presence of outliers in a data base is to apply univariate robust estimation procedures to the residuals of estimated models. Equally problematic as outliers among the response variable are outliers or aberrant values for the predictor variables. Extreme values on individual predictor variables or an unusual combination of predictor variable values for a few observational units can distort least squares estimators even if the error assumptions are valid. This article discusses robust regression procedures, with special emphasis on techniques which are resistant to extreme predictor variable values. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1982
Accession Number
ADA117022

Entities

People

  • Dovalee Dorsett
  • Richard F. Gunst

Organizations

  • Southern Methodist University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Automobiles
  • Coefficients
  • Computer Programs
  • Data Science
  • Data Sets
  • Databases
  • Estimators
  • Information Science
  • New York
  • Probability
  • Regression Analysis
  • Residuals
  • Statistical Algorithms
  • Statistics
  • Surveys
  • United States

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.