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On Usefulness of Maximum Likelihood Estimator Using Incomplete Data

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Measurement and Multivariate Analysis

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Summary

We often encounter with missing data which consist of a complete part and an incomplete part. In this case, there are two types of maximum likelihood estimator. One is the conventional maximum likelihood estimator based on all of the data and another is based on the complete part only, which neglects the incomplete part. Let n and n* be the sample sizes of the complete part and the incompleste part, respectively. It is well-known that the former is asymptotically better than the latter when n*n is constant. However, other cases have not been well-known. This paper shows that the former is better than the latter in other cases in view of higher-order. In addition, this paper illustrates some exact comparison.

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Author information

Authors and Affiliations

  1. The Institute of Statistical Mathematics, 4-6-7, Minami-Azabu, Minato-ku Tokyo, 106-8569, Japan

    Hironori Fujisawa

Authors
  1. Hironori Fujisawa
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Editor information

Editors and Affiliations

  1. Measurement and Evaluation Program CTL, OISE/UT, The University of Toronto, 252 Bloor Street West, M5S 1V6, Toronto, Ontario, Canada

    Shizuhiko Nishisato (Professor Emeritus) (Professor Emeritus)

  2. Center for Information on Statistical Sciences, The Institute of Statistical Mathematics, 4-6-7 Minami Azabu, 106-8569, Minato-ku, Tokyo, Japan

    Yasumasa Baba (Professor) & Koji Kanefuji (Associate Professor) (Professor) &  (Associate Professor)

  3. Information Complexity & Model Selection Department of Statistics, 336 SMC, The University of Tennessee, 37996-0532, Knoxville, TN, USA

    Hamparsum Bozdogan (McKenzie Professor in Business) (McKenzie Professor in Business)

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© 2002 Springer Japan

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Fujisawa, H. (2002). On Usefulness of Maximum Likelihood Estimator Using Incomplete Data. In: Nishisato, S., Baba, Y., Bozdogan, H., Kanefuji, K. (eds) Measurement and Multivariate Analysis. Springer, Tokyo. https://doi.org/10.1007/978-4-431-65955-6_24

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  • DOI: https://doi.org/10.1007/978-4-431-65955-6_24

  • Publisher Name: Springer, Tokyo

  • Print ISBN: 978-4-431-65957-0

  • Online ISBN: 978-4-431-65955-6

  • eBook Packages: Springer Book Archive

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