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Estimation in semiparametric models using an auxiliary model

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Abstract

Two classes of semiparametric and nonparametric mixture models are defined to represent general kinds of prior information. For these models the nonparametric maximum likelihood estimator (NPMLE) of an unknown probability distribution is derived and is shown to be consistent and relative efficient. Linear functionals are used for the estimation of parameters. Their consistency is proved, the gain of efficiency is derived and asymptotical distributions are given.

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Authors and Affiliations

  1. Fakultät Wirtschaftswissenschaften Lehrstuhl für Quantitative Verfahren, insbesondere Statistik, Technische Universität Dresden, 01062, Dresden

    Stefan Huschens

  2. Institut für international vergleichende Wirtschafts-und Sozialstatistik, Universität Heidelberg, Hauptstr 126, 69117, Heidelberg

    Gerhard Stahl

Authors
  1. Stefan Huschens
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  2. Gerhard Stahl
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Huschens, S., Stahl, G. Estimation in semiparametric models using an auxiliary model. Stat Papers 36, 313–326 (1995). https://doi.org/10.1007/BF02926045

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  • DOI: https://doi.org/10.1007/BF02926045

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