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Fitting a Normal Distribution When the Model is Wrong

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Abstract

The paper discusses a likelihood based method of estimation which allows for a small amount of misspecification in the assumption of normality. Asymptotic results suggest that the new method can give an estimated model which is closer to the true model. An application to hearing threshold data is discussed.

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Author information

Authors and Affiliations

  1. Department of Statistics, University of Warwick, Coventry, CV4 7AL, U.K

    J.B. Copas & C.B. Stride

Authors
  1. J.B. Copas
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  2. C.B. Stride
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Copas, J., Stride, C. Fitting a Normal Distribution When the Model is Wrong. Annals of the Institute of Statistical Mathematics 49, 601–614 (1997). https://doi.org/10.1023/A:1003220407410

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  • DOI: https://doi.org/10.1023/A:1003220407410

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