The method proposed by Hadi (1994) for multiple outlier detection in a single group of multivariate data is adapted to the multiple cluster setting. The idea is to replace, in Hadi’s algorithm, the Gaussian distribution and the Mahalanobis distance with the K-component normal mixture model (with K > 1) and a coherent measure of discrepancy from a mixture distribution, respectively. The performance of the proposed procedure is illustrated on a real data set and compared, through a simulation study, with the method proposed by Caroni and Billor (2007) for detecting multiple outliers in grouped multivariate data
Variable selection and other dimensionality reduction methods are more important than ever before. D...
Although it is customary to assume that data are homogeneous, in fact, they often contain outliers o...
We propose a diagnostic method that can be used whenever multiple outliers are identified by robust...
The method proposed by Hadi (1994) for multiple outlier detection in a single group of multivariate...
Outlier identification is important in many applications of multivariate analysis. Either because th...
Mahalanobis-type distances in which the shape matrix is derived from a consistent highbreakdown robu...
This work is motivated by an application in an industrial context, where the activity of sensors is ...
We examine relationships between the problem of robust estimation of multivariate location and shape...
Forward Search methods have been shown to be usefully employed for detecting multiple outliers in co...
This is an Accepted Manuscript of an article published by Taylor & Francis in “ Quality and Reliabil...
This paper makes comparisons of automated procedures for robust multivariate outlier detection throu...
Multivariate outlier detection is usually based on Mahalanobis distances, by plugging in robust est...
Outlier identification often implies inspecting each z-transformed variable and adding a Mahalanobis...
A collection of methods for multivariate outlier detection based on a robust Mahalanobis distance is...
Methodologies for identifying multivariate outliers are extremely important in statistical analysis....
Variable selection and other dimensionality reduction methods are more important than ever before. D...
Although it is customary to assume that data are homogeneous, in fact, they often contain outliers o...
We propose a diagnostic method that can be used whenever multiple outliers are identified by robust...
The method proposed by Hadi (1994) for multiple outlier detection in a single group of multivariate...
Outlier identification is important in many applications of multivariate analysis. Either because th...
Mahalanobis-type distances in which the shape matrix is derived from a consistent highbreakdown robu...
This work is motivated by an application in an industrial context, where the activity of sensors is ...
We examine relationships between the problem of robust estimation of multivariate location and shape...
Forward Search methods have been shown to be usefully employed for detecting multiple outliers in co...
This is an Accepted Manuscript of an article published by Taylor & Francis in “ Quality and Reliabil...
This paper makes comparisons of automated procedures for robust multivariate outlier detection throu...
Multivariate outlier detection is usually based on Mahalanobis distances, by plugging in robust est...
Outlier identification often implies inspecting each z-transformed variable and adding a Mahalanobis...
A collection of methods for multivariate outlier detection based on a robust Mahalanobis distance is...
Methodologies for identifying multivariate outliers are extremely important in statistical analysis....
Variable selection and other dimensionality reduction methods are more important than ever before. D...
Although it is customary to assume that data are homogeneous, in fact, they often contain outliers o...
We propose a diagnostic method that can be used whenever multiple outliers are identified by robust...
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