A note on automatic data transformation
A note on automatic data transformation
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DOI：
10.1002/sta4.104
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发表时间：
2016-01-01
期刊：
影响因子：
1.7
通讯作者：
Marron, J. S.
中科院分区：
文献类型：
--
作者：
Feng, Qing;Hannig, Jan;Marron, J. S.
现代数据分析经常涉及高度非高斯边际分布的变量。然而，常用的分析方法对于粗略的高斯数据最为有效。本文介绍了一种自动变换，它提高了分布对正态分布的接近程度。对于每个变量，提出了一种新的移位对数变换的参数化族，它的独特之处在于将数据视为实数，并允许在单个族内进行左偏和右偏的变换。这还允许通过最小化已转换数据的Anderson-Darling检验统计量来自动选择参数值(这对于具有多个要转换的变量的高维数据至关重要)。对从黑色素瘤显微镜幻灯片中提取的图像特征的应用表明，所提出的变换在处理具有过度偏度、异方差和有影响的观测的数据方面是有用的。版权所有(C)2016 John Wiley&Sons，Ltd.
Modern data analysis frequently involves variables with highly non-Gaussian marginal distributions. However, commonly used analysis methods are most effective with roughly Gaussian data. This paper introduces an automatic transformation that improves the closeness of distributions to normality. For each variable, a new family of parametrizations of the shifted logarithm transformation is proposed, which is unique in treating the data as real valued and in allowing transformation for both left and right skewness within the single family. This also allows an automatic selection of the parameter value (which is crucial for high-dimensional data with many variables to transform) by minimizing the Anderson-Darling test statistic of the transformed data. An application to image features extracted from melanoma microscopy slides demonstrates the utility of the proposed transformation in addressing data with excessive skewness, heteroscedasticity and influential observations. Copyright (C) 2016 John Wiley & Sons, Ltd.