Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99803
Title: Testing structural change in partially linear single-index models with error-prone linear covariates
Authors: Huang, Zhensheng
Pang, Zhen
Hu, Tao
Keywords: Mathematical Sciences
Issue Date: 2012
Source: 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.
Series/Report no.: Computational statistics & data analysis
Abstract: 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.
URI: https://hdl.handle.net/10356/99803
http://hdl.handle.net/10220/17571
ISSN: 0167-9473
DOI: http://dx.doi.org/10.1016/j.csda.2012.10.002
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SPMS Journal Articles

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