Testing structural change in partially linear single-index models with error-prone linear covariates

Motivated by an analysis of a real data set from Duchenne Muscular Dystrophy (Andrews and Herzberg, 1985), we propose a new test of structural change for a class of partially linear single-index models with error-prone linear covariates. Based on the local linear estimation for the unknowns in these...

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Main Authors: Huang, Zhensheng, Pang, Zhen, Hu, Tao
Other Authors: School of Physical and Mathematical Sciences
Format: Article
Language:English
Published: 2013
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Online Access:https://hdl.handle.net/10356/99803
http://hdl.handle.net/10220/17571
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-998032020-03-07T12:34:49Z Testing structural change in partially linear single-index models with error-prone linear covariates Huang, Zhensheng Pang, Zhen Hu, Tao School of Physical and Mathematical Sciences Mathematical Sciences Motivated by an analysis of a real data set from Duchenne Muscular Dystrophy (Andrews and Herzberg, 1985), we propose a new test of structural change for a class of partially linear single-index models with error-prone linear covariates. Based on the local linear estimation for the unknowns in these semiparametric models, we develop a new generalized F-test statistics for the nonparametric part in the partially linear single-index models with error-prone linear covariates. Asymptotic properties of the newly proposed test statistics are proved to follow asymptotically the chi-squared distribution. The new Wilks’ phenomenon is unveiled in a class of semiparametric measure error models. Simulations are conducted to examine the performance of our proposed method. The simulation results are consistent with our theoretical findings. Real data examples are used to illustrate the proposed methodology. 2013-11-11T05:25:23Z 2019-12-06T20:11:48Z 2013-11-11T05:25:23Z 2019-12-06T20:11:48Z 2012 2012 Journal Article Huang, Z., Pang, Z., & Hu, T. (2013). Testing structural change in partially linear single-index models with error-prone linear covariates. Computational Statistics & Data Analysis, 59, 121-133. 0167-9473 https://hdl.handle.net/10356/99803 http://hdl.handle.net/10220/17571 10.1016/j.csda.2012.10.002 en Computational statistics & data analysis
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Mathematical Sciences
spellingShingle Mathematical Sciences
Huang, Zhensheng
Pang, Zhen
Hu, Tao
Testing structural change in partially linear single-index models with error-prone linear covariates
description Motivated by an analysis of a real data set from Duchenne Muscular Dystrophy (Andrews and Herzberg, 1985), we propose a new test of structural change for a class of partially linear single-index models with error-prone linear covariates. Based on the local linear estimation for the unknowns in these semiparametric models, we develop a new generalized F-test statistics for the nonparametric part in the partially linear single-index models with error-prone linear covariates. Asymptotic properties of the newly proposed test statistics are proved to follow asymptotically the chi-squared distribution. The new Wilks’ phenomenon is unveiled in a class of semiparametric measure error models. Simulations are conducted to examine the performance of our proposed method. The simulation results are consistent with our theoretical findings. Real data examples are used to illustrate the proposed methodology.
author2 School of Physical and Mathematical Sciences
author_facet School of Physical and Mathematical Sciences
Huang, Zhensheng
Pang, Zhen
Hu, Tao
format Article
author Huang, Zhensheng
Pang, Zhen
Hu, Tao
author_sort Huang, Zhensheng
title Testing structural change in partially linear single-index models with error-prone linear covariates
title_short Testing structural change in partially linear single-index models with error-prone linear covariates
title_full Testing structural change in partially linear single-index models with error-prone linear covariates
title_fullStr Testing structural change in partially linear single-index models with error-prone linear covariates
title_full_unstemmed Testing structural change in partially linear single-index models with error-prone linear covariates
title_sort testing structural change in partially linear single-index models with error-prone linear covariates
publishDate 2013
url https://hdl.handle.net/10356/99803
http://hdl.handle.net/10220/17571
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