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。血液化学变量 用来区分疾病人群和正常人群 已经被发现具有不对称的分布。使用计算机 模拟，参数(线性和二次)的特性 和非参数(固定和可变核)判别方法 已经被调查的数据来自于一个歪曲的 多元对数正态分布。此外，排序和倒数 将正常分数转换应用于来自 模拟，以确定他们是否可以改进 判别函数的精确度。结果发现， 非参数方法的精度低于参数方法。 方法当数据来自多元对数正态分布时 分发。秩正态分数变换和逆正态分数变换 极大地提高了参数的分类精度 方法：研究方法。 应用了等级和逆正态分数变换 来自多变量重复测量设计的数据，以便 补救非球形性和非正规性对 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 使用重复测量的某些经典测试的性能。

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 variable 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 simulation 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 in order 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.