Transforming variables to central normality
نویسندگان
چکیده
Many real data sets contain numerical features (variables) whose distribution is far from normal (gaussian). Instead, their often skewed. In order to handle such it customary preprocess the variables make them more normal. The Box-Cox and Yeo-Johnson transformations are well-known tools for this. However, standard maximum likelihood estimator of transformation parameter highly sensitive outliers, will try move outliers inward at expense normality central part data. We propose a modification these as well an that robust so transformed can be approximately in center few may deviate it. It compares favorably existing techniques extensive simulation study on
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ژورنال
عنوان ژورنال: Machine Learning
سال: 2021
ISSN: ['0885-6125', '1573-0565']
DOI: https://doi.org/10.1007/s10994-021-05960-5