Bounded-Influence Inference in Regression.

Abstract

Two new classes of tests for regression models, likelihood ratio type tests based on quadratic forms of robust estimators, are introduced. Both can be viewed as generalization of the classical F-test. By means of the influence function their robustness properties are investigated and optimally robust tests that maximize the asymptotic power within each class, under the side condition of a bounded influence function, are constructed. Finally, an example based on real data shows that these tests are valuable robust alternatives to the F-test. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1984
Accession Number
ADA143195

Entities

People

  • E. Ronchetti

Organizations

  • Princeton University

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Analysis Of Variance
  • Atmospheric Temperature
  • Computations
  • Computer Science
  • Covariance
  • Data Science
  • Distribution Functions
  • Efficiency
  • Equations
  • Estimators
  • Information Science
  • Mathematics
  • New York
  • Normality
  • Random Variables
  • Statistical Algorithms
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms