Detecting bivariate outliers on the basis of normalizing transformations for non-Gaussian data

Prykhodko, S.; Prykhodko, N.; Makarova, L.; Kudin, O.; Smykodub, T.; Prykhodko, A.

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Title	Detecting bivariate outliers on the basis of normalizing transformations for non-Gaussian data
Authors	Prykhodko, S. Prykhodko, N. Makarova, L. Kudin, O. Smykodub, T. Prykhodko, A.
ORCID
Keywords	outlier normalizing transformation bivariate non-Gaussian data Mahalanobis squared distance F distribution prediction ellipse
Type	Conference Papers
Date of Issue	2017
URI	http://essuir.sumdu.edu.ua/handle/123456789/55754
Publisher	Sumy State University
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Citation	Detecting bivariate outliers on the basis of normalizing transformations for non-Gaussian data [Текст] / S. Prykhodko, N. Prykhodko, L. Makarova [et al.] // Advanced Information Systems and Technologies : proceedings of the V international scientific conference, Sumy, May 17-19 2017/ Edited by S.І. Protsenko, V.V. Shendryk. - Sumy : Sumy State University, 2017. - P. 95-97.
Abstract	The statistical technique for detecting outliers in bivariate non-Gaussian data on the basis of normalizing transformations, prediction ellipse and a test statistic (TS) for the Mahalanobis squared distance (MSD), which has an approximate F distribution, is proposed. Application of the technique is considered for detecting outliers in two bivariate non-Gaussian data sets: the first, actual effort (hours) and size (adjusted function points) from 145 maintenance and development projects, the second, effort (hours) and mass (tonnes) of designed the section of the ship from 188 designs of sections.
Appears in Collections:	Наукові видання (ЕлІТ)