Multivariate outlier detection for incomplete survey data
modi is a collection of functions for multivariate outlier dedection and imputation.
The aim is to provide a set of functions which cope with missing values and take sampling weights into account.
The original functions were
developed in the EUREDIT project. This work was partially supported by the EU FP5 ICT programme,
the Swiss Federal Office of Education and Science and the Swiss Federal Statistical Office.
Subsequent development was in the AMELI project of the EU FP7 SSH Programme and also supported by the
University of Applied Sciences and Arts Northwestern Switzerland (FHNW).
BACON-EEM algorithm in
BEM(), Epidemic algorithm in
EAimp (), Transformed Rank Correlations in
TRC(), Gaussian imputation with MCD in
C\'edric B\'eguin and Beat Hulliger.
Maintainer: Beat Hulliger <firstname.lastname@example.org>
B\'eguin, C., and Hulliger, B. (2004). Multivariate oulier detection in incomplete survey data: The epidemic algorithm and transformed rank correlations. Journal of the Royal Statistical Society, A 167(Part 2.), 275-294.
B\'eguin, C. and Hulliger, B. (2008) The BACON-EEM Algorithm for Multivariate Outlier Detection in Incomplete Survey Data, Survey Methodology, Vol. 34, No. 1, pp. 91-103.