Strong Consistency of Estimators for Heteroscedastic Partly Linear Regression Model under Dependent Samples

Liang, Han-Ying; Jing, Bing-Yi

doi:https://doi.org/10.1155/S1048953302000187

International Journal of Stochastic Analysis

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Volume 15 |Article ID 537094 | https://doi.org/10.1155/S1048953302000187

Han-Ying Liang, Bing-Yi Jing, "Strong Consistency of Estimators for Heteroscedastic Partly Linear Regression Model under Dependent Samples", International Journal of Stochastic Analysis, vol. 15, Article ID 537094, 13 pages, 2002. https://doi.org/10.1155/S1048953302000187

Strong Consistency of Estimators for Heteroscedastic Partly Linear Regression Model under Dependent Samples

Han-Ying Liang¹ and Bing-Yi Jing²

¹Tongji University, Department of Applied Mathematics, Shanghai 200092, China

²Hong Kong University of Science and Technology, Department of Mathematics, Clear Water Bay, Kowloon, Hong Kong

Received01 Jul 2001

Revised01 Feb 2002

Abstract

In this paper we are concerned with the heteroscedastic regression model yi=xiβ+g(ti)+σiei, 1≤i≤n under correlated errors ei, where it is assumed that σi2=f(ui), the design points (xi,ti,ui) are known and nonrandom, and g and f are unknown functions. The interest lies in the slope parameter β. Assuming the unobserved disturbance ei are negatively associated, we study the issue of strong consistency for two different slope estimators: the least squares estimator and the weighted least squares estimator.

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Copyright © 2002 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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