Abstract
A new procedure for identification of outliers in Tucker3 model is proposed. It is based on robust initialization of the Tucker3 algorithm using Multivariate trimming or Minimum covariance determinant. The performance of the algorithm is tested by a Monte Carlo study on simulated data sets and also on a real data set known to contain outliers.
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