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Asymptotic minimax estimation in semiparametric models
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  • Published: March 1991

Asymptotic minimax estimation in semiparametric models

  • Byeong Uk Park1 nAff2 

Probability Theory and Related Fields volume 88, pages 107–120 (1991)Cite this article

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Summary

We give several conditions on the estimator of efficient score function for estimating the parametric component of semiparametric models. A semiparametric version of the one-step MLE using an estimator of efficient score function which fulfills the conditions is shown to converge to the normal distribution with minimum variance locally uniformly over a fairly large neighborhood around the assumed semiparametric model. Consequently, it is shown to be asymptotically minimax with bounded subconvex loss functions. A few examples are also considered.

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Author notes
  1. Byeong Uk Park

    Present address: Department of Computer Science and Statistics, Seoul National University, Seoul, Korea

Authors and Affiliations

  1. University of North Carolina at Chapel Hill, N.C., USA

    Byeong Uk Park

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  1. Byeong Uk Park
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Park, B.U. Asymptotic minimax estimation in semiparametric models. Probab. Th. Rel. Fields 88, 107–120 (1991). https://doi.org/10.1007/BF01193584

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  • Received: 19 December 1989

  • Revised: 01 March 1990

  • Issue Date: March 1991

  • DOI: https://doi.org/10.1007/BF01193584

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Keywords

  • Normal Distribution
  • Stochastic Process
  • Probability Theory
  • Loss Function
  • Mathematical Biology
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