We introduce asymptotic parameter-free hypothesis tests based on extreme value theory to
detect outlying observations in finite samples. Our tests have nontrivial power for detecting …

This article introduces lookout, a new approach to detect outliers using leave-one-out kernel
density estimates and extreme value theory. Outlier detection methods that use kernel …

Many real-world phenomena generate data sets with outliers ie, extreme observations that
are away from the mainstream of the data. The presence of outliers may cause invalid …

Beyond the challenge of keeping up to date with current best practices regarding the
diagnosis and treatment of outliers, an additional difficulty arises concerning the …

No doubt the reader is well aware that the normal curve plays an integral role in applied
research. Properties of this curve, that are routinely described in every introductory statistics …

Tukey's traditional boxplot (Tukey, 1977) is a widely used Exploratory Data Analysis (EDA)
tools often used for outlier detection with univariate data. In this dissertation, a modification …

Most real-world data sets contain outliers that have unusually large or small values when
compared with others in the data set. Outliers may cause a negative effect on data analyses …

This article introduces DOBIN, a new approach to select a set of basis vectors tailored for
outlier detection. DOBIN has a simple mathematical foundation and can be used as a …

Page 1 VOLUME 16 HOW TO DETECT AND HANDLE OUTLIERS by BORIS IGLEWICZ and
DAVID C. HOAGLIN AS American Society for Quality Control Statistics Division Page 2 Volume …

This 72-page guidebook on how to work with outliers in statistical analyses is a good
introduction to the subject. There are eight chapters in the book starting with the motivation …