Abstract

The mixed variable discriminant analysis procedure assumes that observations are distributed multivariate normal with different group means but same variance-covariance matrix. However, attention has not been given in discriminant analysis when the assumption of normality no longer holds. Therefore, we present a simple but new approach to mixed variable discriminant analysis when available observations (or its mixture) are not distributed multivariate normal. Specifically, a mixture of bernoulli and exponential and, poisson and bernoulli variates in discriminant analysis were presented in this work. Under a given condition, the suggested mixed non normal discriminant procedure demonstrated ability to allocate a mixture of non normal observations with minimal error.

摘要

混合变量判别分析程序假定观察值分布为具有不同组均值但具有相同方差-协方差矩阵的多元正态分布。然而，当正态性假设不再成立时，判别分析就没有引起注意。因此，当可用观察值（或其混合）不是分布多元正态时，我们提出了一种简单但新的混合变量判别分析方法。具体来说，这项工作介绍了判别分析中伯努利和指数变量以及泊松和伯努利变量的混合。在给定条件下，建议的混合非正态判别程序展示了以最小误差分配非正态观察混合的能力。