This paper presents a new univariate decomposition method for design sensitivity analysis and reliabilitybased design optimization of mechanical systems subject to uncertain performance functions in constraints. The method involves a novel univariate approximation of a general multivariate function in the rotated Gaussian space for reliability analysis, analytical sensitivity of failure probabi...

BACKGROUND AND PURPOSE Parameters and indices related to the implant geometry in use for describing the quality of volume implants in interstitial brachytherapy were developed on the basis of LDR-brachytherapy. The aim of our study was to evaluate their usefulness for predicting late toxicity and local control in the PDR-brachytherapy of head-and-neck-tumors. PATIENTS AND METHODS Between Janu...

Univariate decision tree algorithms are widely used in Data Mining because (i) they are easy to learn (ii) when trained they can be expressed in rule based manner. In several applications mainly including Data Mining, the dataset to be learned is very large. In those cases it is highly desirable to construct univariate decision trees in reasonable time. This may be accomplished by parallelizing...

Based on a constructive representation, which distinguishes between a skewing mechanism P and an underlying symmetric distribution F , we introduce two flexible classes of distributions. They are generated by nonparametric modelling of either P or F . We examine properties of these distributions and consider how they can help us to identify which aspects of the data are badly captured by simple...

Stratified medicine seeks to identify biomarkers or parsimonious gene signatures distinguishing patients that will benefit most from a targeted treatment. We evaluated 12 approaches in high-dimensional Cox models in randomized clinical trials: penalization of the biomarker main effects and biomarker-by-treatment interactions (full-lasso, three kinds of adaptive lasso, ridge+lasso and group-lass...