Robust Multiple Linear Regression.

Abstract

An extensive Monte Carlo analysis is conducted to determine the performance of robust linear regression techniques with and without outliers. Thirteen methods of regression are compared including least squares and minimum absolute deviation. The classical robust techniques of Huber, Hampel were studied and robust techniques using the Q-statistic as a discriminant were introduced. The model studied contained eleven variables with 27 observations. The error distributions considered were uniformly normally, double exponentially distributed. Least squares gave the best fit without outliers. In the presence of gross outliers a rejection of outliers technique gave the best fit. (Author)

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1982
Accession Number
ADA124678

Entities

People

  • Ahmed Mohamed M. Sultan

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Human Systems
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Computational Science
  • Data Mining
  • Data Science
  • Estimators
  • Information Science
  • Linear Programming
  • Mathematical Models
  • Mathematics
  • Normal Distribution
  • Operations Research
  • Predictive Modeling
  • Regression Analysis
  • Statistical Algorithms
  • Statistical Inference
  • Statistics
  • Surveys

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.
  • Statistical inference.