Multivariate Outlier Detection and Imputation for Incomplete Survey Data
BACON-EEM Algorithm for multivariate outlier detection in incomplete m...
Utility function for EAdet and EAimp
Epidemic Algorithm for detection of multivariate outliers in incomplet...
Epidemic Algorithm for imputation of multivariate outliers in incomple...
EM for multivariate normal data
Utility for ER function
Robust EM-algorithm ER
Gaussian imputation followed by MCD
Addressing function for Epidemic Algorithm
Addressing function for Epidemic Algorithm
Mahalanobis distance (MD) for data with missing values
modi: Multivariate outlier detection for incomplete survey data.
Non-zero non-missing minimum function
Plot of infection times of the EA algorithm
QQ-Plot of Mahalanobis distances
Nearest Neighbour Imputation with Mahalanobis distance
psi-function
Sweep operator
Transformed rank correlations for multivariate outlier detection
Quantiles of a weighted cdf
Weighted univariate variance coping with missing values
Utility function for TRC.R among others
Winsorization followed by imputation
Algorithms for multivariate outlier detection when missing values occur. Algorithms are based on Mahalanobis distance or data depth. Imputation is based on the multivariate normal model or uses nearest neighbour donors. The algorithms take sample designs, in particular weighting, into account. The methods are described in Bill and Hulliger (2016) <doi:10.17713/ajs.v45i1.86>.