This talk will start with a discussion of the relationships between LASSO estimation, ridge regression, and attenuation due to measurement error as motivation for, and introduction to, a new generalizable approach to variable selection in parametric and nonparametric regression and discriminant analysis. The approach transcends the boundaries of parametric/nonparametric models. It will first be described in the familiar context of linear regression where its relationship to the LASSO will be described in detail. The latter part of the talk will focus on implementation of the approach to nonparametric modeling where sparse dependence on covariates is desired. Applications to two- and multi-category classification problems will be discussed in detail.
Group for Research in Decision Analysis