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Theory of Probability and Mathematical Statistics

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Asymptotic normality of $ L_p$-estimators in nonlinear regression models with weak dependence


Authors: O. V. Ivanov and I. V. Orlovs'kiĭ
Translated by: Oleg Klesov
Original publication: Teoriya Imovirnostei ta Matematichna Statistika, tom 79 (2008).
Journal: Theor. Probability and Math. Statist. 79 (2009), 57-72
MSC (2000): Primary 62J02; Secondary 62J99
DOI: https://doi.org/10.1090/S0094-9000-09-00780-7
Published electronically: December 29, 2009
MathSciNet review: 2494535
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Abstract | References | Similar Articles | Additional Information

Abstract: A theorem on asymptotic normality is proved, and the limit distribution is found for $ L_p$-estimators of a vector parameter in a nonlinear regression model with continuous time and weakly dependent random noise.


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Additional Information

O. V. Ivanov
Affiliation: Department of Mathematical Analysis and Probability Theory, National Technical University of Ukraine, Kyiv Polytechnical Institute, Peremogy Avenue 37, Kyiv-56, 03056, Ukraine
Email: ivanov@paligora.kiev.ua

I. V. Orlovs'kiĭ
Affiliation: Department of Mathematical Analysis and Probability Theory, National Technical University of Ukraine, Kyiv Polytechnical Institute, Peremogy Avenue 37, Kyiv-56, 03056, Ukraine
Email: avalon@ukrpost.ua

DOI: https://doi.org/10.1090/S0094-9000-09-00780-7
Keywords: $L_p$-estimators, asymptotic normality, nonlinear regression models, weak dependence
Received by editor(s): September 11, 2008
Published electronically: December 29, 2009
Article copyright: © Copyright 2009 American Mathematical Society

American Mathematical Society