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Reconstructive Estimation in a Parametric Random Censorship Model With Incomplete Data

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Advances in Statistical Decision Theory and Applications

Part of the book series: Statistics for Industry and Technology ((SIT))

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Abstract

We explore theoretically an approach to estimation, in a multivariate random censorship model with incomplete data, based on the reconstruction of the missing information. Simulation results are also presented.

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Author information

Authors and Affiliations

  1. Universitá degli Studi di Roma “La Sapienza”, Rome, Italy

    Tommaso Gastaldi

Authors
  1. Tommaso Gastaldi
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Editor information

Editors and Affiliations

  1. Department of Math, Southern Illinois University, 62901-4408, Carbondale, IL, USA

    S. Panchapakesan

  2. Department of Mathematics and Statistics, McMaster University, L8S 4K1, Hamilton, Ontario, Canada

    N. Balakrishnan

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© 1997 Birkhäuser Boston

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Gastaldi, T. (1997). Reconstructive Estimation in a Parametric Random Censorship Model With Incomplete Data. In: Panchapakesan, S., Balakrishnan, N. (eds) Advances in Statistical Decision Theory and Applications. Statistics for Industry and Technology. Birkhäuser Boston. https://doi.org/10.1007/978-1-4612-2308-5_25

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  • DOI: https://doi.org/10.1007/978-1-4612-2308-5_25

  • Publisher Name: Birkhäuser Boston

  • Print ISBN: 978-1-4612-7495-7

  • Online ISBN: 978-1-4612-2308-5

  • eBook Packages: Springer Book Archive

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