Best Linear Classification Rule for Multivariate Interval Screened Data

Abstract

In classification analysis, the target variable is often in practice defined by an underlying multivariate interval screening scheme. This engenders the problem of properly characterizing the screened populations as well as that of obtaining a classification procedure. Such problems paved the way for the development of yet another linear classification procedure and the incorporation of a class of skew-elliptical distributions for describing evolutions in the populations. To render the linear procedure effective, this article considers derivation and properties of the classification procedure as well as efficient estimation. The procedure is illustrated in applications to real and simulation data.