Add to ORKGDescription routines for univariate and multivariate outlier detection with a focus on parametric methods, but support for some methods based on resistant statistics Depends R (> = 2.5.0), methods, tools, util
Outlier detection is an important task in statistical analyses. An outlier is a case-specific unit s...
Determining outliers is more complicated in multivariate data sets than it is in univariate cases. T...
Researchers often lack knowledge about how to deal with outliers when analyzing their data. Even mor...
Outlier identification is important in many applications of multivariate analysis. Either because th...
Researchers often lack knowledge about how to deal with outliers when analyzing their data. Even mor...
Multivariate outliers are usually identified by means of robust distances. A statistically principl...
[EN] Deviating multivariate observations are used typically to test the performance of outlier detec...
\u3cp\u3eResearchers often lack knowledge about how to deal with outliers when analyzing their data....
Multivariate outlier identification requires the choice of reliable cut-off points for the robust di...
Outlier identification often implies inspecting each z-transformed variable and adding a Mahalanobis...
Abstract-In many applications outlier detection is an important task. In the process of Knowledge Di...
A number of methods are available to detect outliers in univariate data sets. Most of these tests ar...
Outlier identification is important in many applications of multivariate analysis. Either because th...
Robust statistics has slowly become familiar to all practitioners. Books entirely devoted to the sub...
In extending univariate outlier detection methods to higher dimension, various issues arise: limited...
Outlier detection is an important task in statistical analyses. An outlier is a case-specific unit s...
Determining outliers is more complicated in multivariate data sets than it is in univariate cases. T...
Researchers often lack knowledge about how to deal with outliers when analyzing their data. Even mor...
Outlier identification is important in many applications of multivariate analysis. Either because th...
Researchers often lack knowledge about how to deal with outliers when analyzing their data. Even mor...
Multivariate outliers are usually identified by means of robust distances. A statistically principl...
[EN] Deviating multivariate observations are used typically to test the performance of outlier detec...
\u3cp\u3eResearchers often lack knowledge about how to deal with outliers when analyzing their data....
Multivariate outlier identification requires the choice of reliable cut-off points for the robust di...
Outlier identification often implies inspecting each z-transformed variable and adding a Mahalanobis...
Abstract-In many applications outlier detection is an important task. In the process of Knowledge Di...
A number of methods are available to detect outliers in univariate data sets. Most of these tests ar...
Outlier identification is important in many applications of multivariate analysis. Either because th...
Robust statistics has slowly become familiar to all practitioners. Books entirely devoted to the sub...
In extending univariate outlier detection methods to higher dimension, various issues arise: limited...
Outlier detection is an important task in statistical analyses. An outlier is a case-specific unit s...
Determining outliers is more complicated in multivariate data sets than it is in univariate cases. T...
Researchers often lack knowledge about how to deal with outliers when analyzing their data. Even mor...