The least median of squares (LMS) regression method (Rousseeuw 1984) is highly robust to outliers in the data. It can be computed by means of PROGRESS (from Program for RObust reGRESSion) described in (Rousseeuw and Leroy 1987). After ten years we have developed a new version of PROGRESS, which also computes the least trimmed squares (LTS) method. We will discuss the various new features of PRO...

Shrinkage estimation usually reduces variance at the cost of bias. But when we care only about some parameters of a model, I show that we can reduce variance without incurring bias if we have additional information about the distribution of covariates. In a linear regression model with homoscedastic Normal noise, I consider shrinkage estimation of the nuisance parameters associated with control...

E-Competence is an innovative approach to root and spread eLearning and eServices throughout the University. Key elements are: winning “second-wave” lecturers to eLearning and understanding ECompetence as part of an ongoing strategy towards the digital campus, making eLearning and the adoption of eServices part of everyday life („E-University“). The paper describes the methodical approach and t...

The Least Mean Square (LMS) algorithm, introduced by Widrow and Hoff in 1959 [12] is an adaptive algorithm, which uses a gradient-based method of steepest decent [10]. LMS algorithm uses the estimates of the gradient vector from the available data. LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vect...

An analysis of two LMS-Newton adaptive filtering algorithms with variable convergence factor is presented. The relations of these algorithms with the conventional recursive least-squares algorithm are first addressed. Their performance in stationary and nonstationary environments is then studied and closed-form formulas for the excess mean-square error (MSE) are derived. The paper deals, in add...

The Least Mean Square (LMS) algorithm, introduced by Widrow and Hoff in 1959 [12] is an adaptive algorithm, which uses a gradient-based method of steepest decent [10]. LMS algorithm uses the estimates of the gradient vector from the available data. LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vect...