Outliers detection and treatment: a review

By Denis Cousineau, Sylvain Chartier. 2010

634 citations recorded by the source corpus.

Outliers are observations or measures that are suspicious because they are much smaller or much larger than the vast majority of the observations. These observations are problematic because they may not be caused by the mental process under scrutiny or may not reflect the ability under examination. The problem is that a few outliers is sometimes enough to distort the group results (by altering the mean performance, by increasing variability, etc.). In this paper, various techniques aimed at detecting potential outliers are reviewed. These techniques are subdivided into two classes, the ones regarding univariate data and those addressing multivariate data. Within these two classes, we consider the cases where the population distribution is known to be normal, the population is not normal but known, or the population is unknown. Recommendations will be put forward in each case.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b87177a90e0b4216376ee0f7f6afc1e9177abf3f

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In Management Science and Operations Research

1. Eklas Hossain

Citations in the corpus

• Consumer's perceptions of website's utilitarian and hedonic attributes and online purchase intentions: A cognitive–affective attitude approach (2017, methodology): “Outliers were treated with the mean of corresponding values (Cousineau & Chartier, 2015).”
• Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers (2020, background): “Q 1 , Q 3 , and IQR is the first quartile, third quartile, and interquartile range of the attributes respectively, where The attributes, after outlier rejection, were processed to fill the missing or null values [24] as they could lead to the wrong prediction for any classifiers.”