This note generalizes previous results on the asymptotic equivalence of Ordinary and Generalized Least Squares estimates in Linear Regression models with trending data.

In this paper we describe the principles of polymerase chain reaction (PCR) and its expanding use in molecular genetic research and molecular medicine. A short introduction of exemplary applications of the PCR is connected with a discussion of the lack of PCR accuracy. We give a statistical...

In the common polynomial regression model of degree m we consider the problem of determining the D- and D1-optimal designs subject to certain constraints for the D- efficiencies in the models of degree m – j,m + j , … m + k (m j 0 k 0 given). We present a complete solution of these...

In den Wirtschafts- und Sozialwissenschaften wird die Statistik einmal als Datenlieferant und einmal als Dateninterpretierer wahrgenommen. Während die erste Rolle in Zukunft an Bedeutung eher zunehmen wird, scheint sich die Statistik als Dateninterpretierer von den Bedürfnissen der...

We propose a computer intensive method for linear dimension reduction which minimizes the classification error directly. Simulated annealing (Bohachevsky et al 1986) as a modern optimization technique is used to solve this problem effectively. This approach easily allows to incorporate user...

We propose multivariate classification as a statistical tool to describe business cycles. These cycles are often analyzed as a univariate phenomenon in terms of GNP or industrial net production ignoring additional information in other economic variables. Multivariate classification overcomes...

The best linear unbiased estimator BLUE (CXb) of a linear transform CX b in the general Gauss-Markov model (y, E (y) = X b Cov (y) =a2v) is the linear transform C BLUE (Xb) of the best linear unbiased estimator BLUE (Xb) of Xb. Similarly, for the ordinary least squares estimator OLSE (CXb) = C...

Support Vector Machines (SVMs) have become a popular tool for learning with large amounts of high dimensional data. However, it may sometimes be preferable to learn incrementally from previousSVM results, as computing a SVM is very costly in terms of time and memory consumption or because the...