Journal ArticleDOI
Detecting multivariate outliers: Use a robust variant of the Mahalanobis distance
TLDR
A variant based on the Minimum Covariance Determinant, a more robust procedure that is easy to implement and demonstrates the detrimental impact of outliers on parameter estimation and shows the superiority of the MCD over the Mahalanobis distance.About:
This article is published in Journal of Experimental Social Psychology.The article was published on 2018-01-01. It has received 212 citations till now. The article focuses on the topics: Mahalanobis distance & Robust statistics.read more
Citations
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Journal ArticleDOI
How to classify, detect, and manage univariate and multivariate outliers, with emphasis on pre-registration
TL;DR: A functional definition of outlier detection methods is provided and the use of the median absolute deviation to detect univariate outliers, and of the Mahalanobis-MCD distance to detect multivariate outlier outliers is recommended.
Journal ArticleDOI
Together Apart: The Mitigating Role of Digital Communication Technologies on Negative Affect During the COVID-19 Outbreak in Italy
Alessandro Gabbiadini,Cristina Baldissarri,Federica Durante,Roberta Rosa Valtorta,Maria De Rosa,Marcello Gallucci +5 more
TL;DR: In this paper, the authors investigated whether the amount of digital communication technology use for virtual meetings (i.e., voice and video calls, online board games and multiplayer video games, or watching movies in party mode) during the lockdown promoted the perception of social support, which in itself mitigated the psychological effects of the lockdown in Italy.
Outlier removal, sum scores, and the inflation of the Type I error rate
Marjan Bakker,Jelte M. Wicherts +1 more
TL;DR: Results of simulations of artificial and actual psychological data are presented, which show that the removal of outliers based on commonly used Z value thresholds severely increases the Type I error rate.
Journal ArticleDOI
Mahalanobis distance and its application for detecting multivariate outliers
TL;DR: In this paper, after short reviewing some tools for univariate outliers detection, the Mahalanobis distance, as a famous multivariate statistical distances, and its ability to detect multivariate outsiers are discussed.
Journal ArticleDOI
Attack detection in water distribution systems using machine learning
Daniel Ramotsoela,Gerhard P. Hancke,Gerhard P. Hancke,Adnan M. Abu-Mahfouz,Adnan M. Abu-Mahfouz +4 more
TL;DR: Traditional anomaly detection techniques are evaluated in the context of attack detection in water distribution systems and a novel ensemble technique that combines density-based and parametric algorithms is developed.
References
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A power primer.
TL;DR: A convenient, although not comprehensive, presentation of required sample sizes is providedHere the sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests.
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Robust Regression and Outlier Detection
TL;DR: This paper presents the results of a two-year study of the statistical treatment of outliers in the context of one-Dimensional Location and its applications to discrete-time reinforcement learning.
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False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant
TL;DR: It is shown that despite empirical psychologists’ nominal endorsement of a low rate of false-positive findings, flexibility in data collection, analysis, and reporting dramatically increases actual false- positive rates, and a simple, low-cost, and straightforwardly effective disclosure-based solution is suggested.