STATISTICAL DISCRIMINANT METHODS WITH APPLICATIONS TO ALCOHOLISM SCREENING

这项研究调查了各种统计特性， 判别函数，并确定它们区分的程度 酒精中毒者、其他疾病患者和正常人群之间， 血液化学的标准电池。 血液化学 用于区分疾病和 已发现正态组具有偏态分布。 使用计算机模拟，参数（线性）的性质 和二次）和非参数（固定和变量核） 当数据到达时， 偏态多变量对数正态分布。 此外，本发明还提供了一种方法， 秩和逆正态分数变换应用于 刺激的数据，以确定他们是否可以 提高判别函数的精度。 这是 发现非参数方法的准确性低于 当数据来自多变量时， 对数正态分布 秩和逆正态分数 变换大大提高了分类精度， 参数化方法。 秩和逆正态分数变换已被 应用于多变量重复测量设计的数据 为了弥补非球形和非正态性的影响， 经典的重复测量分析。 它是通过 模拟结果表明，逆正态分数确实提高了 某些经典测试的性能， 措施

This study investigates the statistical properties of a variety of discriminant functions and determines how well they differentiate between alcoholic, other diseased, and normal populations using standard batteries of blood chemistries. Blood chemistry variables that are used to discriminate between diseased and normal groups have been found to have skewed distributions. Using computer simulations, the properties of parametric (linear and quadratic) and nonparametric (fixed and variables kernel) discriminant methods have been investigated when the data comes from a skewed multivariate lognormal distribution. In addition, rank and inverse normal score transformations were applied to the data from the stimulation in order to determine if they could improve upon the accuracy of the discriminant functions. It was found that the nonparametric methods were less accurate than the parametric methods when the data came from a multivariate lognormal distribution. The rank and inverse normal score transformations greatly improved the classification accuracy of the parametric methods. The rank and inverse normal score transformations have been applied to data from multivariate repeated measure designs inorder to remedy the effect nonsphericity and non-normality has upon classical repeated measure analyses. It was shown through simulations that the inverse normal scores does improve the performance of certain classical tests used with repeated measures.