In this section, you can access to the latest technical information related to the FUTURE project topic.

A new outlier detection method for spherical data

Abstract

In this study, we propose a new method to detect outlying observations in spherical data. The method is based on the k-nearest neighbours distance theory. The proposed method is a good alternative to the existing tests of discordancy for detecting outliers in spherical data. In addition, the new method can be generalized to identify a patch of outliers in the data. We obtain the cut-off points and investigate the performance of the test statistic via simulation. The proposed test performs well in detecting a single and a patch of outliers in spherical data. As an illustration, we apply the method on an eye data set.

» Author: Adzhar Rambli,  Ibrahim Bin Mohamed,  Abdul Ghapor Hussin

» Reference: https://doi.org/10.1371/journal.pone.0273144

» Publication Date: 24/08/2022

» More Information

« Go to Technological Watch



AIMPLAS Instituto Tecnológico del Plástico

C/ Gustave Eiffel, 4
(València Parc Tecnològic) - 46980
PATERNA (Valencia) - SPAIN

PHONE

(+34) 96 136 60 40

EMAIL

Project Management department - Sustainability and Industrial Recovery
life-future-project@aimplas.es