Statistics and Data Science Seminar
Lingsong Zhang
Purdue University
Sparse Distance Weighted Discrimination and the Oracle Theory
Abstract: Distance Weighted Discrimination (DWD) has recently been proposed as an
attractive classification method. In this paper, we first show Fisher
consistency of the DWD method, which justifies its use when there are
sufficient data. However, the DWD classifier is not sparse, which makes
the interpretation and prediction performance less attractive. We
propose several sparse DWD methods, which incorporate variable selection
techniques in classification using penalized loss functions to estimate
the true hyperplane. We show that when an appropriate penalty is used,
the sparse DWD method is consistent and the estimated normal vector has
the oracle property under suitable conditions. We evaluate the finite
sample performance of the proposed methods using simulations and
illustrate the methods with an application to the Faroe island proteomic
biomarker data.
Wednesday September 8, 2010 at 3:00 PM in SEO 636