Robust Estimation in Heteroscedastic Linear Models.

Abstract

We consider a heteroscedastic linear model in which the variances are a parametric function of the mean responses and a parameter theta. We propose robust estimates for the regression parameter beta and show that, as long as a reasonable starting estimate of theta is available, our estimates of beta are asymptotically equivalent to the natural estimate obtained with known variances. A particular method for estimating theta is proposed and shown by Monte-Carlo to work quite well, especially in power and exponential models for the variances. We also briefly discuss a 'feedback' estimate of beta. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1981
Accession Number
ADA096413

Entities

People

  • David Ruppert
  • Raymond J. Carroll

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Confidence Limits
  • Covariance
  • Data Science
  • Distribution Functions
  • Estimators
  • Feedback
  • Information Science
  • Maximum Likelihood Estimation
  • Normal Distribution
  • North Carolina
  • Standards
  • Statistical Algorithms
  • Statistical Inference
  • Statistics
  • Theorems

Fields of Study

  • Mathematics

Readers

  • Statistical inference.