 Testing structural change in partially linear single-index models with error-prone linear covariatesZhensheng Huang, Zhen Pang and Tao Hu Computational Statistics & Data Analysis, 2013, vol. 59, issue C, 121-133 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. Keywords: Chi-squared distribution; F-type test; Local linear method; Measure error; Single-index model (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:59:y:2013:i:c:p:121-133 DOI: 10.1016/j.csda.2012.10.002 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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