Summary. We use the forward search to provide robust Mahalanobis distances to detect the presence of outliers in a sample of multivariate normal data. Theoretical results on order sta-tistics and on estimation in truncated samples provide the distribution of our test statistic. We also introduce several new robust distances with associated distributional results. Comparisons of our procedure with tests using other robust Mahalanobis distances show the good size and high power of our procedure. We also provide a unification of results on correction factors for estimation from truncated samples
We present a semi-automatic method of outlier detection for continuous, multivariate survey data. In...
Detecting outliers in high dimension datasets remains a challenging task.Under this circumstance, ro...
Outlier identification is important in many applications of multivariate analysis. Either because th...
We use the forward search to provide robust Mahalanobis distances to detect the presence of outlier...
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...
Robust distances are mainly used for the purpose of detecting multivariate outliers. The precise def...
A collection of methods for multivariate outlier detection based on a robust Mahalanobis distance is...
While methods of detecting outliers is frequently implemented by statisticians when analyzing univar...
This book is about using graphs to explore and model continuous multivariate data. Such data are oft...
This paper makes comparisons of automated procedures for robust multivariate outlier detection throu...
Outlier identification is important in many applications of multivariate analysis. Either because th...
Multivariate outliers are usually identified by means of robust distances. A statistically principl...
The classical estimators of multivariate location and scatter for the normal model are the sample m...
Determining outliers is more complicated in multivariate data sets than it is in univariate cases. T...
We present a semi-automatic method of outlier detection for continuous, multivariate survey data. In...
Detecting outliers in high dimension datasets remains a challenging task.Under this circumstance, ro...
Outlier identification is important in many applications of multivariate analysis. Either because th...
We use the forward search to provide robust Mahalanobis distances to detect the presence of outlier...
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...
Robust distances are mainly used for the purpose of detecting multivariate outliers. The precise def...
A collection of methods for multivariate outlier detection based on a robust Mahalanobis distance is...
While methods of detecting outliers is frequently implemented by statisticians when analyzing univar...
This book is about using graphs to explore and model continuous multivariate data. Such data are oft...
This paper makes comparisons of automated procedures for robust multivariate outlier detection throu...
Outlier identification is important in many applications of multivariate analysis. Either because th...
Multivariate outliers are usually identified by means of robust distances. A statistically principl...
The classical estimators of multivariate location and scatter for the normal model are the sample m...
Determining outliers is more complicated in multivariate data sets than it is in univariate cases. T...
We present a semi-automatic method of outlier detection for continuous, multivariate survey data. In...
Detecting outliers in high dimension datasets remains a challenging task.Under this circumstance, ro...
Outlier identification is important in many applications of multivariate analysis. Either because th...