Evaluating Alteration in Regression Coefficients Directed by Low-effect Variables

Abstract

[evdate 2013-12-19] HKUST International Forum on Probability and Statistics. Talk no. 12. Title from slide title.

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.

Abstract: It has been a long history of utilizing interactions in regression analysis to investigate alterations in covariate-effects on response variables. In the talk, the speaker aims to address two kinds of new challenges resulted from the inclusion of such high-order effects in the regression model for complex data. The first kind arises from a situation where interaction effects of individual covariates are weak but those of combined covariates are strong, and the other kind pertains to the presence of nonlinear interactive effects directed by low-effect covariates. The speaker proposes a new class of semiparametric models with varying index coefficients, which enables us to model and assess nonlinear interaction effects between grouped covariates on the response variable. As a result, most of the existing semiparametric regression models are special cases of our proposed models. The speaker develop a numerically stable and computationally fast estimation procedure utilizing both profile least squares method and local fitting. The talk establish both estimation consistency and asymptotic normality for the proposed estimators of index coefficients as well as the oracle property for the nonparametric function estimator.

Duration: 38 min.