Resistant estimates for high dimensional and functional data based on random projections

We herein propose a new robust estimation method based on random projections that is adaptive and automatically produces a robust estimate, while enabling easy computations for high or infinite dimensional data. Under some restricted contamination models, the procedure is robust and attains full efficiency. We tested the method using both simulated and real data.Fil: Fraiman, Jacob Ricardo. Universidad de San Andrés; Argentina. Universidad de la República; Uruguay. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Svarc, Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentin

Interpretable Clustering using Unsupervised Binary Trees

We herein introduce a new method of interpretable clustering that uses unsupervised binary trees. It is a three-stage procedure, the first stage of which entails a series of recursive binary splits to reduce the heterogeneity of the data within the new subsamples. During the second stage (pruning), consideration is given to whether adjacent nodes can be aggregated. Finally, during the third stage (joining), similar clusters are joined together, even if they do not descend from the same node originally. Consistency results are obtained, and the procedure is used on simulated and real data sets.Comment: 25 pages, 6 figure

Feature Selection for Functional Data

In this paper we address the problem of feature selection when the data is functional, we study several statistical procedures including classification, regression and principal components. One advantage of the blinding procedure is that it is very flexible since the features are defined by a set of functions, relevant to the problem being studied, proposed by the user. Our method is consistent under a set of quite general assumptions, and produces good results with the real data examples that we analyze.Comment: 22 pages, 4 figure

On the existence of bounded solutions for a nonlinear elliptic system

This work deals with the system $(-\Delta)^m u= a(x) v^p$, $(-\Delta)^m v=b(x) u^q$ with Dirichlet boundary condition in a domain \Omega\subset\RR^n, where $\Omega$ is a ball if $n\ge 3$ or a smooth perturbation of a ball when $n=2$. We prove that, under appropriate conditions on the parameters ($a,b,p,q,m,n$), any non-negative solution $(u,v)$ of the system is bounded by a constant independent of $(u,v)$. Moreover, we prove that the conditions are sharp in the sense that, up to some border case, the relation on the parameters are also necessary. The case $m=1$ was considered by Souplet in \cite{PS}. Our paper generalize to $m\ge 1$ the results of that paper

On well-posedness of the Cauchy problem for the wave equation in static spherically symmetric spacetimes

We give simple conditions implying the well-posedness of the Cauchy problem for the propagation of classical scalar fields in general (n+2)-dimensional static and spherically symmetric spacetimes. They are related to properties of the underlying spatial part of the wave operator, one of which being the standard essentially self-adjointness. However, in many examples the spatial part of the wave operator turns out to be not essentially selfadjoint, but it does satisfy a weaker property that we call here quasi essentially self-adjointness, which is enough to ensure the desired well-posedness. This is why we also characterize this second property. We state abstract results, then general results for a class of operators encompassing many examples in the literature, and we finish with the explicit analysis of some of them.Comment: 36 pages. Final version to appear in Classical and Quantum Gravit

Defeasible reasoning in dynamic domains

The design of intelligent agents is a key issue for many applications. Since there is no universally accepted definition of intelligence, the notion of rational agency was proposed by Russell as an alternative for the characterization of intelligent agency. A rational agent must have models of itself and its surroundings to use them in its reasoning. To this end, this paper develops a formalism appropriate to represent the knowledge of an agent. Moreover, if dynamic environments are considered, the agent should be able to observe the changes in the world, and integrate them into its existing beliefs. This motivates the incorporation of perception capabilities into our framework. The abilities to perceive and act, essential activities in a practica! agent, demand a timely interaction with the environment. Given that the cognitive process of a rational agent is complex and computationally expensive, this interaction may not be easy to achieve. To solve this problem, we propase inference mechanisms that rely on the use precompiled knowledge to optimize the reasoning process.I Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

An argumentation framework with uncertainty management designed for dynamic environments

Nowadays, data intensive applications are in constant demand and there is need of computing environments with better intelligent capabilities than those present in today's Database Management Systems (DBMS). To build such systems we need formalisms that can perform complicate inferences, obtain the appropriate conclusions, and explain the results. Research in argumentation could provide results in this direction, providing means to build interactive systems able to reason with large databases and/or di erent data sources. In this paper we propose an argumentation system able to deal with explicit uncertainty, a vital capability in modern applications. We have also provided the system with the ability to seamlessly incorporate uncertain and/or contradictory information into its knowledge base, using a modular upgrading and revision procedurePresentado en el X Workshop Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI