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Detección de M señales gaussianas utilizando el desarrollo modificado de un proceso estocástico.

Jesús Navarro Moreno, Juan Carlos Ruiz Molina (2001)

Qüestiió

Utilizando el desarrollo modificado de un proceso estocástico se propone una nueva metodología, alternativa a la basada en el desarrollo de Karhunen-Loeve, para el problema de detección de M señales Gaussianas en ruido Gaussiano blanco. Las soluciones proporcionadas no presentan el problema del cálculo de los autovalores y autofunciones asociados a la función de covarianza involucrada y son fácilmente implementables desde el punto de vista práctico.

Detección de rasgos en imágenes binarias mediante procesos puntuales espaciales marcados.

Jorge Mateu, Gil Lorenzo (2002)

Qüestiió

En este trabajo consideramos el problema de la detección de rasgos bajo la presencia de ruido en imágenes que tras un cierto tratamiento se reducen a binarias, por la presencia de dos tipos de elementos. Podemos encontrar ejemplos de este problema en la detección de minas por medio de imágenes de avión o satélite, en la búsqueda de rasgos en imágenes microscópicas de células, o en la caracterización de fallas en zonas de terremotos.En primer lugar revisamos algunos métodos de detección jerárquicos...

Detection of transient change in mean – a linear behavior inside epidemic interval

Daniela Jarušková (2011)

Kybernetika

A procedure for testing occurrance of a transient change in mean of a sequence is suggested where inside an epidemic interval the mean is a linear function of time points. Asymptotic behavior of considered trimmed maximum-type test statistics is presented. Approximate critical values are obtained using an approximation of exceedance probabilities over a high level by Gaussian fields with a locally stationary structure.

Deterministic characterization of viability for stochastic differential equation driven by fractional brownian motion

Tianyang Nie, Aurel Răşcanu (2012)

ESAIM: Control, Optimisation and Calculus of Variations

In this paper, using direct and inverse images for fractional stochastic tangent sets, we establish the deterministic necessary and sufficient conditions which control that the solution of a given stochastic differential equation driven by the fractional Brownian motion evolves in some particular sets K. As a consequence, a comparison theorem is obtained.

Deviation inequalities and moderate deviations for estimators of parameters in bifurcating autoregressive models

S. Valère Bitseki Penda, Hacène Djellout (2014)

Annales de l'I.H.P. Probabilités et statistiques

The purpose of this paper is to investigate the deviation inequalities and the moderate deviation principle of the least squares estimators of the unknown parameters of general p th-order asymmetric bifurcating autoregressive processes, under suitable assumptions on the driven noise of the process. Our investigation relies on the moderate deviation principle for martingales.

Differentiability of excessive functions of one-dimensional diffusions and the principle of smooth fit

Paavo Salminen, Bao Quoc Ta (2015)

Banach Center Publications

The principle of smooth fit is probably the most used tool to find solutions to optimal stopping problems of one-dimensional diffusions. It is important, e.g., in financial mathematical applications to understand in which kind of models and problems smooth fit can fail. In this paper we connect-in case of one-dimensional diffusions-the validity of smooth fit and the differentiability of excessive functions. The basic tool to derive the results is the representation theory of excessive functions;...

Differential equations driven by gaussian signals

Peter Friz, Nicolas Victoir (2010)

Annales de l'I.H.P. Probabilités et statistiques

We consider multi-dimensional gaussian processes and give a new condition on the covariance, simple and sharp, for the existence of Lévy area(s). gaussian rough paths are constructed with a variety of weak and strong approximation results. Together with a new RKHS embedding, we obtain a powerful – yet conceptually simple – framework in which to analyze differential equations driven by gaussian signals in the rough paths sense.

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