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Statistical Inference on Partially Linear Additive Models with Missing Response Variables and Error-prone Covariates.

Statistical Inference on Partially Linear Additive Models with Missing Response Variables and Error-prone Covariates.

Authors :
Wei, Chuan-Hua
Jia, Xu-Jie
Hu, Hong-Sheng
Source :
Communications in Statistics: Theory & Methods. 2015, Vol. 44 Issue 4, p872-883. 12p.
Publication Year :
2015

Abstract

This paper considers statistical inference for the partially linear additive models, which are useful extensions of additive models and partially linear models. We focus on the case where some covariates are measured with additive errors, and the response variable is sometimes missing. We propose a profile least-squares estimator for the parametric component and show that the resulting estimator is asymptotically normal. To construct a confidence region for the parametric component, we also propose an empirical-likelihood-based statistic, which is shown to have a chi-squared distribution asymptotically. Furthermore, a simulation study is conducted to illustrate the performance of the proposed methods. [ABSTRACT FROM PUBLISHER]

Details

Language :
English
ISSN :
03610926
Volume :
44
Issue :
4
Database :
Academic Search Index
Journal :
Communications in Statistics: Theory & Methods
Publication Type :
Academic Journal
Accession number :
101157728
Full Text :
https://doi.org/10.1080/03610926.2012.735327