The Within-Cluster Sum of Squares (WCSS) is the most used criterion in cluster analysis. Optimizing this criterion is proved to be NP-Hard and has been studied by different communities. On the other hand, Constrained Clustering allowing to integrate previous user knowledge in the clustering process has received much attention this last decade. As far as we know, there is a single approach that ...

Large outliers break down linear and nonlinear regression models. Robust regression methods allow one to filter out the outliers when building a model. By replacing the traditional least squares criterion with the least trimmed squares criterion, in which half of data is treated as potential outliers, one can fit accurate regression models to strongly contaminated data. High-breakdown methods h...

In this brief note, we establish a novel criterion for robustness of global asymptotic stability zero solution LTV system $\dot x=A(t)x$ in the presence possibly unbounded perturbations (external disturbances). To prove result, logarithmic norm will be used under which becomes topological notion depending on chosen vector state-space $\mathbb{R}^n.$

The Modified Information Criterion (MIC) is an Akaike-like criterion which allows performance control by means of a simple a priori defined parameter, the upper-bound on the error of the first kind (false alarm probability). The criterion MIC is for example used to estimate the order of Auto-Regressive (AR) processes. The criterion can only be used to test pairs of composite hypotheses; in an A...

T he world economy has experienced a bulk of positive and negative shocks in crude oil prices and exchange rates over the years, and that global inflation has undergone some changes. Such shocks have affected the macroeconomic variables in the countries of the world and have challenged the economies of these countries, and have led them to take different measures to protect themselves agai...

We evaluate the performance of a numerical method for global optimization of expensive functions. The method is using a response surface to guide the search for the global optimum. This metamodel could be based on radial basis functions, kriging, or a combination of different models. We discuss how to set the cyclic parameters of the optimization method to get a balance between local and global...