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Asymptotic theory in generalized ilinear models with nuisance scale parameters
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  • Published: March 1992

Asymptotic theory in generalized ilinear models with nuisance scale parameters

  • Jun Shao1 

Probability Theory and Related Fields volume 91, pages 25–41 (1992)Cite this article

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Summary

This paper establishes the asymptotic normality and the consistencyrobustness of the weighted least squares estimator (WLSE) in the generalized linear models with multiple nuisance scale parameters. In addition, noting that the asymptotic robust statistical inference in presence of nuisance scale parameters requires a consistency-robust estimator of the asymptotic covariance matrix of the WLSE, this paper derives a class of covariance estimators and proves their consistency-robustness.

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Author information

Authors and Affiliations

  1. Department of Mathematics, University of Ottawa, K1N 6N5, Ottawa, Ontario, Canada

    Jun Shao

Authors
  1. Jun Shao
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Additional information

This research was supported by an operating grant from the Natural Science and Engineering Research Council of Canada and the United States NSF-AFOSR grant ISSA-860068

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Cite this article

Shao, J. Asymptotic theory in generalized ilinear models with nuisance scale parameters. Probab. Th. Rel. Fields 91, 25–41 (1992). https://doi.org/10.1007/BF01194488

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  • Received: 30 August 1989

  • Revised: 14 July 1990

  • Issue Date: March 1992

  • DOI: https://doi.org/10.1007/BF01194488

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Keywords

  • Covariance
  • Linear Model
  • Covariance Matrix
  • Stochastic Process
  • Generalize Linear Model
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