STATISTICAL DISCRIMINANT METHODS WITH APPLICATIONS TO ALCOHOLISM SCREENING

这项研究调查了各种不同的统计特性 判别函数，并确定它们的区分程度 在酗酒者、其他疾病患者和正常人群之间使用 血液化学的标准电池。血液化学 用于区分疾病和疾病的变量 正态群已被发现具有偏态分布。 利用计算机模拟，参数(线性)的性质 和二次)和非参数(固定和变量核) 当数据到来时，对判别方法进行了研究 从一个不对称的多元对数正态分布。此外, 等级和逆正态得分变换被应用于 来自刺激的数据，以确定他们是否可以 提高了判别函数的精度。确实是 发现非参数方法的精确度不如 多变量数据下的参数方法 对数正态分布。秩次与逆正态得分 变换极大地提高了分类精度 参数方法。 等级和逆正态分数变换已经被 应用于多变量重复测量设计的数据 为了弥补非球形性和非正则性的影响 基于经典的重复测量分析。它是通过 模拟表明，反向正态得分确实提高了 重复使用的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.