Pareto-Optimality and the Desirability Index are methods for multicriteria optimization in quality management. In this paper the pareto-optimality of the optimal influence factor settings of a process resulting from maximizing the DI is analyzed and is shown to be valid in most cases.

Problem: Cigarette smoking is the most important risk factor of transitional cell carcinoma of the urinary bladder. The effect of the glutathione S- transferases M1 (GSTM1) and M3 (GSTM3) on the influence of this risk factor was investigated. Methods: A total of 293 bladder cancer patients from...

In this note we present a direct and simple approach to obtain bounds on the asymptotic minimax risk for the estimation of restrained binominal and multinominal proportions. Quadratic, normalized quadratic and entropy loss are considered and it is demonstrated that in all cases linear estimators...

We develop and test a robust procedure for extracting an underlying signal in form of a time-varying trend from very noisy time series. The application we have in mind is online monitoring data measured in intensive care, where we find periods of relative constancy, slow monotonic trends, level...

Aiming at optimal prediction of the correct note corresponding to a vocal time series we trained a classification algorithm on the basis of parts of interpretations of Tochter Zion (Händel) and tested the algorithm on the remaining parts. As classification algorithm we use a radial basis...

In this paper, we consider three major types of nonparametric regression tests that are based on kernel and local polynomial smoothing techniques. Their asymptotic power comparisons are established systematically under the fixed and contiguous alternatives, and are also illustrated through...

Potentially adverse human and environmental effects due to hormone mimicry of environmental estrogens are a matter of current concern. Environmental estrogens belong to the so-called endocrine active compounds (EAC), which alter signalling processes of the endocrine system leading to a broad...

The analysis of temporal data is an important issue of current research, because most real-world data either explicitly or implicitly contains some information about time. The key to successfully solving temporal learning tasks is to analyze the assumptions that can be made and prior knowledge...

Motivated in part by applications in model selection in statistical genetics and sequential monitoring of financial data, we study an empirical process framework for a class of stopping rules which rely on kernel-weighted averages of past data. We are interested in the asymptotic distribution...