M-Estimates for the Heteroscedastic Linear Model.

Abstract

We treat a linear model for a parameter Theta. For simultaneous M-estimates we find the limit distribution. For the special case of least squares estimation, this limit distribution is the same as the limit distribution of the weighted least squares, and in general the distribution is that of a weighted M-estimate using these weights. Moreover, the covariance matrix of the limit distribution can be consistently estimated, so large sample confidence ellipsoids and tests of hypotheses are feasible.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1979
Accession Number
ADA077889

Entities

People

  • David Ruppert
  • Raymond J. Carroll

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Covariance
  • Data Science
  • Data Sets
  • Distribution Functions
  • Ellipsoids
  • Estimators
  • Gaussian Distributions
  • Hypotheses
  • Information Science
  • Intervals
  • New York
  • Normal Distribution
  • North Carolina
  • Probability
  • Scientific Research
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Statistical inference.