dc.contributor.authorHuang, Zhensheng
dc.contributor.authorPang, Zhen
dc.contributor.authorHu, Tao
dc.date.accessioned2013-11-11T05:25:23Z
dc.date.available2013-11-11T05:25:23Z
dc.date.copyright2012en_US
dc.date.issued2012
dc.identifier.citationHuang, 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.en_US
dc.identifier.issn0167-9473en_US
dc.identifier.urihttp://hdl.handle.net/10220/17571
dc.description.abstractMotivated 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.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesComputational statistics & data analysisen_US
dc.subjectMathematical Sciences
dc.titleTesting structural change in partially linear single-index models with error-prone linear covariatesen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.csda.2012.10.002


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