Statistical Inference on Seemingly Unrelated Single-Index Regression Models

In this article, we consider a class of seemingly unrelated single-index regression models. By taking the contemporaneous correlation among equations into account we construct the weighted estimators （WEs） for unknown parameters of the coefficients and the improved local polynomial estimators for th...

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Published in:Acta Mathematicae Applicatae Sinica 2016-10, Vol.32 (4), p.945-956
Main Authors: He, Bing, You, Jin-hong, Chen, Min
Format: Article
Language:English
Subjects:
Applications of Mathematics
Math Applications in Computer Science
Mathematical and Computational Physics
Mathematics
Mathematics and Statistics
Regression models
Theoretical
加权估计
单指标
回归模型
局部多项式
方程
未知函数
渐近正态性
统计推断
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You, Jin-hong
Chen, Min
description In this article, we consider a class of seemingly unrelated single-index regression models. By taking the contemporaneous correlation among equations into account we construct the weighted estimators （WEs） for unknown parameters of the coefficients and the improved local polynomial estimators for the unknown functions, respectively. We establish the asymptotic normalities of these estimators, and show both of them are more asymptotically efficient than those ignoring the contemporaneous correlation. The performances of the proposed procedures are evaluated through simulation studies.
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subjects Applications of Mathematics
Math Applications in Computer Science
Mathematical and Computational Physics
Mathematics
Mathematics and Statistics
Regression models
Theoretical
加权估计
单指标
回归模型
局部多项式
方程
未知函数
渐近正态性
统计推断
title Statistical Inference on Seemingly Unrelated Single-Index Regression Models
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