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
MathSciNet review:
3364121
Full-text PDF Free Access
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