Robust Regression using Maximum-Likelihood Weighting and Assuming Cauchy-Distributed Random Error.

Abstract

Least-squares estimates of regression coefficients are extremely sensitive to large errors in even a single data point. Frequently, an ad-hoc procedure is used to weight the data in a manner of alleviate the effects of extreme observations. This thesis is a study of the effectiveness of an iterative regression method using weights derived through maximum-likelihood arguments. Actual weights are calculated on the assumption of Cauchy-distributed error as a worst-case situation in which the errors have long, fat tails and no finite moments. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1977
Accession Number
ADA045132

Entities

People

  • Harry Richard Moore Ii

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Advanced Electronics

DTIC Thesaurus Topics

  • Algorithms
  • California
  • Coefficients
  • Data Analysis
  • Data Storage Systems
  • Deficiencies
  • Equations
  • Estimators
  • Histograms
  • Information Science
  • Iterations
  • Observation
  • Operations Research
  • Plastic Explosives
  • Probability
  • Statistical Algorithms
  • United States

Fields of Study

  • Mathematics

Readers

  • Exercise and Sports Science.
  • Psychometric Testing or Psychological Assessment.
  • Statistical inference.