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Activity Number: 236
Type: Topic Contributed
Date/Time: Tuesday, July 31, 2007 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract - #309151
Title: On Single-Index Models
Author(s): Yan Yu*+ and Zhou Wu and Haiqun Lin
Companies: University of Cincinnati and University of Cincinnati and Yale University
Address: , Cincinnati, OH, ,
Keywords: roughness penalty ; multivariate nonparametric estimation ; penalized spline ; local linear ; quantile regression
Abstract:

Single-index models are an important tool for multivariate nonparametric estimation. By reducing the dimensionality from that of a general covariate vector to a univariate index, single-index models avoid the so-called "curse of dimensionality." In this talk, firstly I will give a brief overview of single-index models. Secondly I will introduce single-index varying coefficient models for both regression and dependent data, where we focus on the estimation, inference, and forecasting of the single-index coefficient models under dependence by a penalized spline approach (P-spline). Finally I will discuss some recent results for single-index conditional quantiles using local linear estimation. Both real data applications and simulation studies will be presented.

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Revised September, 2007