Partially Linear Single-index Transformation Model

Abstract

[evdate 2013-12-19] HKUST International Forum on Probability and Statistics. Talk no. 14. Title from opening screen.

The Second HKUST International Forum on Probability and Statistics (2013), held 19 December, 2013, at the Hong Kong University of Science and Technology. Co-sponsored by the HKUST Jockey Club Institute for Advanced Study and the Center for Statistical Science.

'Collaborated with Xiaobo Ding and Xiao-Hua Zhou.'

Abstract: The talk propose a constrained least square estimator of the transformation function of a partially linear single-index transformation model, where the transformation function, single-index function and error distribution are all nonparametric. The estimators of the regression coefficients and the single-index function are provided by the similar idea to the minimum average variance estimation method. Basis function approximation is employed to estimate the transformation and single-index functions, and cross validation criteria are proposed to select suitable sets of basis functions. Asymptotical properties of the estimators in the sense of almost sure convergence are established. Simulations studies show that our proposed estimators work well. A real-world data analysis of total health care charges was used to illustrate the proposed procedure.

Duration: 34 min.