Variable selection in the presence of outliers may be performed by using a robust version of Akaike's information criterion AIC. In this paper explicit expressions are obtained for such criteria when S and MM-estimators are used. The performance of these criteria is compared to the existing AIC...

This papers offers a theoretical explanation for the stylized fact that forecast combinations with estimated optimal weights often perform poorly in applications. The properties of the forecast combination are typically derived under the assumption that the weights are fixed, while in practice...

This paper is about S-estimation for penalized regression splines. Penalized regression splines are one of the currently most used methods for smoothing noisy data. The estimation method used for fitting such a penalized regression spline model is mostly based on least squares methods, which are...

This paper offers a theoretical explanation for the stylized fact that forecast combinations with estimated optimal weights often perform poorly in applications. The properties of the forecast combination are typically derived under the assumption that the weights are fixed, while in practice...

We study estimation and model selection on both the fixed and the random effects in the setting of linear mixed models using outlier robust S-estimators. Robustness aspects on the level of the random effects as well as on the error terms is taken into account. The derived marginal and...

This papers offers a theoretical explanation for the stylized fact that forecast combinations with estimated optimal weights often perform poorly in applications. The properties of the forecast combination are typically derived under the assumption that the weights are fixed, while in practice...