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Robust test statistics for the two-way MANOVA based on the minimum covariancedeterminant (MCD) estimator are proposed as alternatives to the classicalWilks' Lambda test statistics which are well known to be very sensitive tooutliers as they are based on classical normal theory estimates of generalizedvariances. The classical Wilks' Lambda statistics are robustified by replacingthe classical estimates by highly robust and efficient reweighted MCDestimates. Further, Monte Carlo simulations are used to evaluate theperformance of the new test statistics under various designs by investigatingtheir finite sample accuracy, power, and robustness against outliers. Finally,these robust test statistics are applied to a real data example.

Robust test statistics for the two-way MANOVA based on the minimum covariance determinant estimator