The authors propose a new least{squares design procedure for multirate FIR lters with any desired shape of the (band{limited) frequency response. The aliasing, inherent in such systems, is implicitly taken into account in the approximation criterion.

A bias-correction method for closed-loop identi"cation, introduced in the literature as the bias-eliminated least-squares (BELS) method (Zheng & Feng, Automatica 31 (1995) 1019), is shown to be equivalent to a basic instrumental variable estimator applied to a predictor for the closed-loop system. This predictor is a function of the plant parameters and the known controller. Corresponding to th...

For the purpose of exploring and modelling the relationships between a dataset and several datasets, multiblock Partial Least Squares is a widely-used regression technique. It is designed as an extension of PLS which aims at linking two datasets. In the same vein, we propose an extension of Redundancy Analysis to the multiblock setting. We show that PLS and multiblock Redundancy Analysis aim at...

Pixel unmixing is commonly performed by employing a least squared (LS) error criterion, making it sensitive to outliers. As an alternative, the least median of squares (LMedS) method is proposed. Not only is it extremely robust, but it is efficient and straightforward both to implement and use.

Many industrial experiments involve restricted rather than complete randomization. This often leads to the use of split-plot designs, which limit the number of independent settings of some of the experimental factors. These factors, named whole-plot factors, are often, in some way, hard to change. The remaining factors, called sub-plot factors, are easier to change. Their levels are therefore i...

In this paper the problem of determining optimal designs for least squares estimation is considered in the common linear regression model with correlated observations. Our approach is based on the determination of ‘nearly’ universally optimal designs, even in the case where the universally optimal design does not exist. For this purpose we introduce a new optimality criterion which reflects the...

The goal of active learning is to determine the locations of training input points so that the generalization error is minimized. We discuss the problem of active learning in linear regression scenarios. Traditional active learning methods using least-squares learning often assume that the model used for learning is correctly specified. In many practical situations, however, this assumption may...

Mean Squared Error (MSE) has been the most widely used tool to solve the linear filter estimation or system identification problem. However, MSE gives biased results when the input signals are noisy. This paper presents a novel Error Whitening Criterion (EWC) to tackle the problem of linear system identification in the presence of additive white disturbances. We will motivate the theory behind ...