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Adaptive estimation for a semiparametric model of mixture


Author: O. V. Doronin
Translated by: N. Semenov
Original publication: Teoriya Imovirnostei ta Matematichna Statistika, tom 91 (2014).
Journal: Theor. Probability and Math. Statist. 91 (2015), 29-41
MSC (2010): Primary 62G05, 62G20, 62F12; Secondary 62P25, 62G30
DOI: https://doi.org/10.1090/tpms/964
Published electronically: February 3, 2016
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Abstract: A model of mixture with varying concentrations is considered. It is assumed that the first $ K$ of $ M$, $ 1\le K\le M$, components of the mixture are parameterized. A technique of the adaptive semiparametric estimation is developed by using the generalized estimating equations. It is proved that the estimators are consistent and asymptotically normal.


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Additional Information

O. V. Doronin
Affiliation: Department of Probability Theory, Statistics, and Actuarial Mathematics, Faculty for Mechanics and Mathematics, National Taras Shevchenko University, Volodymyrs’ka Street, 64, Kyiv 01601, Ukraine
Email: al_doronin@ukr.net

DOI: https://doi.org/10.1090/tpms/964
Keywords: Adaptive estimation, model of mixture, generalized estimating equations
Received by editor(s): March 4, 2014
Published electronically: February 3, 2016
Article copyright: © Copyright 2016 American Mathematical Society