Discriminant analysis for two data sets in IR d with probability densities f and g can be based on the estimation of the set G = fx : f(x) g(x)g. We consider applications where it is appropriate to assume that the region G has a smooth boundary. In particular, this assumption makes sense if discriminant analysis is used as a data analytic tool. We discuss optimal rates for estimation of G.

Word Count: 47 Manuscript Word Count: 4387 Repeated measures discriminant analysis 2

the main focus in this study is on data pre-processing, reduction in number of inputs or input space size reduction the purpose of which is the justified generalization of data set in smaller dimensions without losing the most significant data. in case the input space is large, the most important input variables can be identified from which insignificant variables are eliminated, or a variable ...

This paper presents a procedure for studying industrial performance and related issues such as changes in the wage structure. This procedure combines cluster analysis and discriminant analysis as a package, and applies this package to time series data. This enables us to organize industrial data into groups with similar wage or performance histories and then to extract summary time-series showi...

This article utilizes data envelopment analysis (DEA) to estimate the productivity of the English and Welsh police forces and to determine whether there are categorical scale effects in policing using multiple discriminant analysis (MDA). The article demonstrates that by using DEA efficiency results it is possible to make inferences about the optimal size and structure of the English and Welsh ...

2 Classification of One-Dimensional Data 2 2.1 Linear Discriminant Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.1.1 Building the LDA Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.1.2 Results of One-Dimensional LDA Classification . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Quadratic Discriminant Analysis . . . . . ....