A pattern recognition technique associated with a new state estimator is developed in order to supervise electrical process. For this purpose, diagnostic features are extracted from current and voltage measurements for monitoring different operating modes. Then, a feature selection method is applied in order to select the most relevant features which define the feature space. In this frame, the...

In clinical trials examining the incidence of pneumonia it is a common practice to measure infection via both invasive and non-invasive procedures. In the context of a recently completed randomized trial comparing two treatments the invasive procedure was only utilized in certain scenarios due to the added risk involved, and given that the level of the non-invasive procedure surpassed a given t...

A parametric version of the Borwein-Preiss smooth variational principle is presented, which states that under suitable assumptions on a given convex function depending on a parameter, the minimum point of a smooth convex perturbation of it depends continuously on the parameter. Some applications are given: existence of a Nash equilibrium and a solution of a variational inequality for a system o...

In this paper, we consider a nonparametric permutation test on the correlation coefficient, which is applied to a significance test on regression coefficients. Because the permutation test is very computerintensive, there are few studies on small-sample properties, although we have numerous studies on asymptotic properties with regard to various aspects. In this paper, we aim to compare the per...

This paper presents a non-parametric method for testing departures from a uniform seasonal variation in incidence of disease. The method may be used either with exact dates of incidence when they are available or with monthly totals. It is equally valid for sinusoidal and non-sinusoidal departures from uniformity. A simulation study shows it to be much more powerful than other non-parametric al...

We develop a non-parametric method to monitor several process variables simultaneously. This method is based on the concept of data depth, which indicates the position of an observation with respect to the data cloud. Principal components are used to reduce the dimensionality of the process, and a non-parametric chart is used to monitor some of the principal components instead of the original v...

We o er a simple paradigm for tting models, parametric and non-parametric, to noisy data, which resolves some of the problems associated with classic MSE algorithms. This is done by considering each point on the model as a possible source for each data point. The paradigm also allows to solve problems which are not de ned in the classical MSE approach, such as tting a segment (as opposed to a l...

We investigate Bayesian non-parametric inference of the Λ-measure of Λ-coalescent processes with recurrent mutation, parametrised by probability measures on the unit interval. We give verifiable criteria on the prior for posterior consistency when observations form a time series, and prove that any non-trivial prior is inconsistent when all observations are contemporaneous. We then show that th...