Semiparametric inference on partially linear single-index model

Zhensheng Huang, Riquan Zhang and Yazhao Lv

Communications in Statistics - Theory and Methods, 2016, vol. 45, issue 14, 4068-4085

Abstract: We consider semiparametric inference on the partially linearsingle-index model (PLSIM). The generalized likelihood ratio (GLR) test is proposed to examine whether or not a family of new semiparametric models fits adequately our given data in the PLSIM. A new GLR statistic is established to deal with the testing of the index parameter α0 in the PLSIM. The newly proposed statistic is shown to asymptotically follow a χ2-distribution with the scale constant and the degrees of freedom being independent of the nuisance parameters or function. Some finite sample simulations and a real example are used to illustrate our proposed methodology.

Date: 2016
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DOI: 10.1080/03610926.2014.915045

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