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...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
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| Online Access: | https://hdl.handle.net/10356/99803 http://hdl.handle.net/10220/17571 |
| _version_ | 1811679352706826240 |
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| author | Huang, Zhensheng Pang, Zhen Hu, Tao |
| author2 | School of Physical and Mathematical Sciences |
| author_facet | School of Physical and Mathematical Sciences Huang, Zhensheng Pang, Zhen Hu, Tao |
| author_sort | Huang, Zhensheng |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T03:07:48Z |
| format | Journal Article |
| id | ntu-10356/99803 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:07:48Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-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 |
| spellingShingle | Mathematical Sciences Huang, Zhensheng Pang, Zhen Hu, Tao Testing structural change in partially linear single-index models with error-prone linear covariates |
| title | 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_short | 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 |
| topic | Mathematical Sciences |
| url | https://hdl.handle.net/10356/99803 http://hdl.handle.net/10220/17571 |
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