Math & Stats Colloquium: A Semiparametric Approach to Dimension Reduction, Estimation, Inference and Efficiency

Title:
A Semiparametric Approach to Dimension Reduction, Estimation, Inference and Efficiency

Guest:
Prof. Yanyuan Ma, Department of Statistics, University of South Carolina

Location:
ARTS 108

Day and Time:
September 5 2014, 3:30 PM

Abstract: We provide a novel and completely different approach to
dimension-reduction problems from the existing literature. We cast the
dimension- reduction problem in a semiparametric estimation framework and
derive estimating equations. Viewing this problem from the new angle
allows us to derive a rich class of estimators, and obtain the classical
dimension reduction techniques as special cases in this class. The
semiparametric approach also reveals that in the inverse regression
context while keeping the estimation structure intact, the common
assumption of linearity and/or constant variance on the covariates can be
removed at the cost of performing additional nonparametric regression. The
semiparametric estimators without these common assumptions are illustrated
through simulation studies and a real data example. This article has
online supplementary material.

POSTER HERE!