This article proposes a class of weighted multivariate normal distributions whose probability density function has the form of a product of a multivariate normal density and a weighting function. The class is obtained from marginal distributions of various doubly truncated multivariate normal...

We describe a formal approach to constructing the optimal classification rule for classification analysis with unknown prior probabilities ofKmultivariate normal populations membership. This is done by suggesting a balanced design for the classification experiment and by constructing the optimal...

Statistical methods for an asymmetric normal classification do not adapt well to the situations where the population distributions are perturbed by an interval-screening scheme. This paper explores methods for providing an optimal classification of future samples in this situation. The...

In normal classification analysis, there may be cases where the population distributions are perturbed by a screening scheme. This paper considers a new classification method for screened data that is obtained from the perturbed normal distributions. Properties of each population distribution is...

This paper describes a threshold classification with <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$K$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mi>K</mi> </math> </EquationSource> </InlineEquation> populations whose membership category is associated with the threshold process of a latent variable. It is seen that the optimal procedure (Bayes procedure) for the classification involves a nonlinear classification rule and...</equationsource></equationsource></inlineequation>