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

Full-text PDF

Abstract | References | Similar Articles | Additional Information

Abstract: A model of mixture with varying concentrations is considered. It is assumed that the first of , , 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