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Conditional information for an inverse Gaussian distribution with known coefficient of variation

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Abstract

Conditional inference about a mean of an inverse Gaussian distribution with known coefficient of variation is discussed. For a random sample from the distribution, sufficient statistics with respect to the mean parameter include an ancillary statistic. The effects of conditioning on the ancillary statistic are investigated. It is shown that the model provides a good illustration of R. A. Fisher's recommendation concerning use of the observed second derivative of the log likelihood function in normal approximations.

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Author information

Authors and Affiliations

  1. The Institute of Statistical Mathematics, 4-6-7 Minami-Azabu, Minato-ku, 106, Tokyo, Japan

    Katuomi Hirano

  2. Department of Applied Mathematics, Faculty of Engineering, Hiroshima University, 724, Higashi-Hiroshima, Japan

    Kōsei Iwase

Authors
  1. Katuomi Hirano
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  2. Kōsei Iwase
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Additional information

This work was started while Kōsei Iwase was visiting the Institute of Statistical Mathematics in Spring, 1987, and was partly supported by the ISM Cooperative Research Program (88-ISM·CRP-7), and by Scientific Research Fund No. 62540173 from the Ministry of Education, Science and Culture of Japan.

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Hirano, K., Iwase, K. Conditional information for an inverse Gaussian distribution with known coefficient of variation. Ann Inst Stat Math 41, 279–287 (1989). https://doi.org/10.1007/BF00049396

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  • DOI: https://doi.org/10.1007/BF00049396

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