Detecting outlying observations is an important step in any analysis, even when robust estimates are used. In particular, the robustified Mahalanobis distance is a natural measure of outlyingness if one focuses on ellipsoidal distributions. However, it is well known that the asymptotic chi-square approximation for the cutoff value of the Mahalanobis distance based on several robust estimates (l...

We propose a principal components regression method based on maximizing joint pseudo-likelihood for responses and predictors. Our uses both predictors to select linear combinations of the relevant regression, thereby addressing an oft-cited deficiency conventional regression. The proposed estimator is shown be consistent in wide range settings, including ones with non-normal dependent observati...

Principal components analysis is a well-known statistical method in dealing with large dependent data sets. It is also used in functional data for both purposes of data reduction as well as variation representation. On the other hand "handwriting" is one of the objects, studied in various statistical fields like pattern recognition and shape analysis. Considering time as the argument,...