Abstract
Based on the ordered values of the total dissimilarity of each observation from all the others, we present a nonparametric method for detection of high dimensional outliers. We provide algorithms to obtain the distribution of the test statistic based on the percentile bootstrap and offer an outlier visualisation plot as a nonparametric graphical tool for detecting outliers in a data set. We compare the interpoint distance outlier test (IDOT) with five competing methods under four distributions, and using a real data set. IDOT shows the best performance for outlier detection in terms of the average number of the outliers detected and the probability of the correct identification.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
