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Robust selection of variables in linear discriminant analysis

机译：线性判别分析中的变量选择

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摘要

A commonly used procedure for reduction of the number of variables in linear discriminant analysis is the stepwise method for variable selection. Although often criticized, when used carefully, this method can be a useful prelude to a further analysis. The contribution of a variable to the discriminatory power of the model is usually measured by the maximum likelihood ratio criterion, referred to as Wilks' lambda. It is well known that the Wilks' lambda statistic is extremely sensitive to the influence of outliers. In this work a robust version of the Wilks' lambda statistic will be constructed based on the Minimum Covariance Discriminant (MCD) estimator and its reweighed version which has a higher efficiency. Taking advantage of the availability of a fast algorithm for computing the MCD a simulation study will be done to evaluate the performance of this statistic.
机译：线性判别分析中减少变量数量的常用方法是逐步选择变量的方法。尽管经常受到批评，但是如果谨慎使用，此方法可能是进行进一步分析的有用前奏。变量对模型判别力的贡献通常通过最大似然比标准（称为威尔克斯λ）来衡量。众所周知，威尔克斯的lambda统计数据对异常值的影响极为敏感。在这项工作中，将基于最小协方差判别器（MCD）估计器和具有更高效率的重新加权版本来构造威尔克斯lambda统计量的可靠版本。利用可用于计算MCD的快速算法的优势，将进行仿真研究以评估此统计数据的性能。

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