Asymptotic Robustness in Regression and Autoregression Based on Lindeberg Conditions

Abstract

A statistical procedure is asymptotically robust if its large-sample properties hold under conditions more general than the conditions under which the procedure is derived. The justification of such procedures is often based directly or indirectly on a central limit theorem. In this paper Lindeberg-type conditions are utilized to establish asymptotic normality of sample regression and autoregression coefficients.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1989
Accession Number
ADA210725

Entities

People

  • Naoto Kunitomo
  • Theodore W. Anderson

Organizations

  • Stanford University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Analysis Of Variance
  • Asymptotic Normality
  • Coefficients
  • Covariance
  • Data Science
  • Estimators
  • Information Science
  • Military Research
  • Multivariate Analysis
  • New York
  • Normality
  • Probability
  • Random Variables
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Inference
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Mathematical Modeling and Probability Theory.
  • Statistical inference.