On a high-dimensional nonlinear stochastic partial differential equation

In this paper we investigate a nonlinear stochastic partial differential equation (spde in short) perturbed by a space-correlated Gaussian noise in arbitrary dimension $d\geq1$, with a non-Lipschitz coefficient noisy term. The equation studied coincides in one dimension with the stochastic Burgers equation. Existence of a weak solution is established through an approximation procedure

Large deviations for a fractional stochastic heat equation in spatial dimension Rd\mathbb{R}^d driven by a spatially correlated noise

In this paper we study the Large Deviation Principle (LDP in abbreviation) for a class of Stochastic Partial Differential Equations (SPDEs) in the whole space $\mathbb{R}^d$, with arbitrary dimension $d\geq 1$, under random influence which is a Gaussian noise, white in time and correlated in space. The differential operator is a fractional derivative operator. We prove a large deviations principle for our equation, using a weak convergence approach based on a variational representation of functionals of infinite-dimensional Brownian motion. This approach reduces the proof of LDP to establishing basic qualitative properties for controlled analogues of the original stochastic system.Comment: This paper has been accepted for publication in Stochastics & Dynamics. This reprint differs from the original in pagination and typographic detail. arXiv admin note: text overlap with arXiv:1309.1935 by other author

A Mechanism for Void Avoidance in Real-Time Routing oriented Medical Applications

To avoid the negative impact of void areas (i.e. holes)on medical application routing efficiency, we propose a neworiented void avoidance mechanism for wireless sensor networks embedded in medical environment. To choose the forwarding region (clockwise or anticlockwise) around the void, proposed mechanism is guided by the destination location with respect to the void. Our mechanism uses the right-hand rule to discover boundary nodes of the void and geometric formulas to obtain the forwarding region of a source node near the void. This node reduces its forwarding candidate set according to its already obtained forwarding region. Proposed approach is simple to implement, economic and could incorporate various other optimizations studies. Simulation results showed the effectiveness of the proposed mechanism which gives better performancecompared to traditional schemes

The robusTest package: two-sample tests revisited

The R package robusTest offers corrected versions of several common tests in bivariate statistics. We point out the limitations of these tests in their classical versions, some of which are well known such as robustness or calibration problems, and provide simple alternatives that can be easily used instead. The classical tests and theirs robust alternatives are compared through a small simulation study. The latter emphasizes the superiority of robust versions of the test of interest. Finally, an illustration of correlation's tests on a real data set is also provided

On a stochastic partial differential equation with non-local diffusion

In this paper, we prove existence, uniqueness and regularity for a class of stochastic partial differential equations with a fractional Laplacian driven by a space-time white noise in dimension one. The equation we consider may also include a reaction term

Sur quelques tests usuels en statistique bivariée

We consider some well known hypothesis tests in bivariate analysis. We emphasize the limitations of these tests, some of them being well known (problems of robustness or calibration), and we propose simple alternatives that can be easily presented to students.Nous considérons plusieurs tests usuels en statistique bivariée. Nous soulignons les limites de ces tests, dont certaines sont bien connues (problèmes de robustesse ou de calibration), et nous proposons des alternatives simples qui peuvent être facilement présentées aux étudiants