Outlier identification is important in many applications of multivariate analysis. Either because there is some specific interest in finding anomalous observations or as a pre-processing task before the application of some multivariate method, in order to preserve the results from possible harmful effects of those observations. It is also of great interest in discriminant analysis if, when predictin
A look at the psychology literature reveals that researchers still seem to encounter difficulties in...
Outlier identification often implies inspecting each z-transformed variable and adding a Mahalanobis...
Abstract: The recent developments by considering a rather unexpected application of the theory of In...
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...
In investigations on the behaviour of robust estimators, typically their consistency and their asymp...
In investigations on the behaviour of robust estimators, typically their consistency and their asymp...
Robust statistics has slowly become familiar to all practitioners. Books entirely devoted to the sub...
Multivariate outlier identification requires the choice of reliable cut-off points for the robust di...
One approach to identifying outliers is to assume that the outliers have a different distribution fr...
In this article, we present a simple multivariate outlier-detection procedure and a robust estimator...
The classical estimators of multivariate location and scatter for the normal model are the sample m...
A look at the psychology literature reveals that researchers still seem to encounter difficulties in...
Before implementing any multivariate statistical analysis based on empirical covariance matrices, it...
A look at the psychology literature reveals that researchers still seem to encounter difficulties in...
Outlier identification often implies inspecting each z-transformed variable and adding a Mahalanobis...
Abstract: The recent developments by considering a rather unexpected application of the theory of In...
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...
In investigations on the behaviour of robust estimators, typically their consistency and their asymp...
In investigations on the behaviour of robust estimators, typically their consistency and their asymp...
Robust statistics has slowly become familiar to all practitioners. Books entirely devoted to the sub...
Multivariate outlier identification requires the choice of reliable cut-off points for the robust di...
One approach to identifying outliers is to assume that the outliers have a different distribution fr...
In this article, we present a simple multivariate outlier-detection procedure and a robust estimator...
The classical estimators of multivariate location and scatter for the normal model are the sample m...
A look at the psychology literature reveals that researchers still seem to encounter difficulties in...
Before implementing any multivariate statistical analysis based on empirical covariance matrices, it...
A look at the psychology literature reveals that researchers still seem to encounter difficulties in...
Outlier identification often implies inspecting each z-transformed variable and adding a Mahalanobis...
Abstract: The recent developments by considering a rather unexpected application of the theory of In...