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2016 Local $M$-estimation for conditional variance function with dependent data
Yunyan Wang, Mingtian Tang
Rocky Mountain J. Math. 46(1): 333-356 (2016). DOI: 10.1216/RMJ-2016-46-1-333

Abstract

In this paper, a local $M$-estimation for the conditional variance function in heteroscedastic regression models under stationary $\alpha $-mixing dependent samples is developed. The local $M$-estimator is based on the local linear smoothing technique and the $M$-estimation technique, and it is shown to be not only asymptotically equivalent to the local linear estimator but also robust. The weak consistency as well as the asymptotic normality of the local $M$-estimator for the conditional variance function are obtained under mild conditions.

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Yunyan Wang. Mingtian Tang. "Local $M$-estimation for conditional variance function with dependent data." Rocky Mountain J. Math. 46 (1) 333 - 356, 2016. https://doi.org/10.1216/RMJ-2016-46-1-333

Information

Published: 2016
First available in Project Euclid: 23 May 2016

zbMATH: 1338.62155
MathSciNet: MR3506093
Digital Object Identifier: 10.1216/RMJ-2016-46-1-333

Subjects:
Primary: 62G20, 62G35

Rights: Copyright © 2016 Rocky Mountain Mathematics Consortium

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Vol.46 • No. 1 • 2016
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