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A quadraticity limit theorem useful in linear models
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  • Published: August 1989

A quadraticity limit theorem useful in linear models

  • Hira L. Koul1 

Probability Theory and Related Fields volume 82, pages 371–386 (1989)Cite this article

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Summary

This paper proves an asymptotic uniform quadraticity of certain statistics based on randomly weighted residual empirical processes under fairly general conditions. This result is useful in many statistical inference problems pertaining to linear models including the Correlation and Autoregression models. Some applications to the goodness of fit tests and minimum distance estimation in linear models are given.

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Authors and Affiliations

  1. Department of Statistics and Probability, Michigan State University, 48824, East Lansing, Michigan, USA

    Hira L. Koul

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  1. Hira L. Koul
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Koul, H.L. A quadraticity limit theorem useful in linear models. Probab. Th. Rel. Fields 82, 371–386 (1989). https://doi.org/10.1007/BF00339993

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  • Received: 05 May 1986

  • Revised: 06 January 1989

  • Issue Date: August 1989

  • DOI: https://doi.org/10.1007/BF00339993

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Keywords

  • Linear Model
  • Stochastic Process
  • General Condition
  • Probability Theory
  • Limit Theorem
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