Add to ORKGWe present a semi-automatic method of outlier detection for continuous, multivariate survey data. In large datasets, outliers may be dicult to nd using informal inspection and graph-ical displays, particularly when there are missing values. Our method relies on an explicit probability model for the data. The raw data with outliers is described by a contaminated multivariate normal distribution, and an EM algorithm is applied to obtain robust estimates of the means and covariances. Mahalanobis distances are computed to identify potential out-liers. The procedure is implemented in a software product which detects outliers and suggests edits to remove oending values. We apply the algorithm to body-measurement data fro
Given a data set arising from a series of observations, an outlier is a value that deviates substant...
Outlier identification is important in many applications of multivariate analysis. Either because th...
Most outlier detection rules for multivariate data are based on the assumption of elliptical symmetr...
As a part of the EUREDIT project new methods to detect multivariate outliers in incomplete survey da...
Outlier identification is important in many applications of multivariate analysis. Either because th...
Summary. We use the forward search to provide robust Mahalanobis distances to detect the presence of...
A look at the psychology literature reveals that researchers still seem to encounter difficulties in...
A look at the psychology literature reveals that researchers still seem to encounter difficulties in...
The classical estimators of multivariate location and scatter for the normal model are the sample m...
Outlier identification often implies inspecting each z-transformed variable and adding a Mahalanobis...
Multivariate outlier detection, Robust statistics, Missing values, 62G35, 62D05, 62H99,
International audienceThe increasing ubiquity of multivariate functional data (MFD) requires methods...
Before implementing any multivariate statistical analysis based on em- pirical covariance matrices, ...
A collection of methods for multivariate outlier detection based on a robust Mahalanobis distance is...
A statistical technique and the necessary computer program for editing multivariate data are present...
Given a data set arising from a series of observations, an outlier is a value that deviates substant...
Outlier identification is important in many applications of multivariate analysis. Either because th...
Most outlier detection rules for multivariate data are based on the assumption of elliptical symmetr...
As a part of the EUREDIT project new methods to detect multivariate outliers in incomplete survey da...
Outlier identification is important in many applications of multivariate analysis. Either because th...
Summary. We use the forward search to provide robust Mahalanobis distances to detect the presence of...
A look at the psychology literature reveals that researchers still seem to encounter difficulties in...
A look at the psychology literature reveals that researchers still seem to encounter difficulties in...
The classical estimators of multivariate location and scatter for the normal model are the sample m...
Outlier identification often implies inspecting each z-transformed variable and adding a Mahalanobis...
Multivariate outlier detection, Robust statistics, Missing values, 62G35, 62D05, 62H99,
International audienceThe increasing ubiquity of multivariate functional data (MFD) requires methods...
Before implementing any multivariate statistical analysis based on em- pirical covariance matrices, ...
A collection of methods for multivariate outlier detection based on a robust Mahalanobis distance is...
A statistical technique and the necessary computer program for editing multivariate data are present...
Given a data set arising from a series of observations, an outlier is a value that deviates substant...
Outlier identification is important in many applications of multivariate analysis. Either because th...
Most outlier detection rules for multivariate data are based on the assumption of elliptical symmetr...