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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
License
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)

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