Defensa de la tesis doctoral de María Jesús Gisbert

15 noviembre, 2018
11:00 ama12:00 pm
11:00 ama12:00 pm

Title: “Sensitivity Analysis and Lipschitzian Properties in Linear Optimization”.

Author: María Jesús Gisbert Francés (Universidad Miguel Hernández).

Organizer: María Josefa Cánovas Cánovas.

Directors: Dra. María Josefa Cánovas Cánovas (Universidad Miguel Hernández de Elche) y Dr. Francisco Javier Toledo Melero (Universidad Miguel Hernández de Elche).

Date: jueves 15 de noviembre, 11:00 horas.

Localication: Sala Alexander von Humboldt (Edificio Torregaitán).

Others: Doctoranda con mención internacional.

Seminario de Alexandre Carvalho

9 noviembre, 2018
11:00 ama12:00 pm
11:00 ama12:00 pm

Speaker: Alexandre Carvalho (Universidad de São Paulo).

Title: “Gradient Structure of a Non-autonomous Chafee-Infante Problems”.

Date: viernes 9 de noviembre, 11:00 horas.

Localication: Aula de seminarios del CIO (Edificio Torretamarit).

Abstract. In this work we prove that some non-autonomous scalar one dimensional semi-linear parabolic problems have an associated skew-product semiflow with gradient structure. The aim is to exhibit a non-autonomous problem for which the asymptotic dynamics can be fairly described.

Seminario de Francisco Marcellán

20 noviembre, 2018
12:00 pma1:00 pm

Speaker: Francisco Marcellán (Universidad Carlos III de Madrid).

Title: “A friendly approach to orthogonal polynomials and their applications”.

Date: martes 20 de noviembre, 12:00 horas.

Localication: Aula 0.2 del CIO (Edificio Torretamarit).

Abstract. The theory of orthogonal polynomials constitutes a nice example of the interplay between classical analysis, numerical analysis, linear algebra, probability theory and mathematical physics and update the classical monographs and constitute good approaches to those topics from different points of view. In this talk we will deal with orthogonal polynomials associated with measures supported on the real line with a special emphasis on the situations where they are eigenfunctions of higher order linear differential operators. Analytic properties of such polynomials in a more general framework will be discussed. Some applications in quadrature rules and boundary value problems will be analyzed. The link with integrable systems (Toda lattices) will be shown following. On the other hand, orthogonal polynomials with respect to nontrivial probability measures supported on the unit circle appear in the study of best linear predictors in filter theory. We will review some methods concerning the generation of such polynomials, their asymptotic behavior as well as the distribution of their zeros. Some applications to Szegő quadratures and integrable systems, in particular Schur flows, will be presented.

Seminario de Allan Seheult

29 octubre, 2018
11:00 ama12:00 pm

Speaker: Allan Seheult (Durham University).

Title: “Bayesian Method of Moments (BMOM) Analysis of Mean and Regression Models”.

Date: Lunes 29 de octubre, 11:00 horas.

Localication: Sala de seminarios del CIO (Edificio Torretamarit).

Abstract. A Bayesian method of moments/instrumental variable (BMOM/IV) approach is developed and applied in the analysis of the important mean and multiple regression models. Given a single set of data, it is shown how to obtain posterior and predictive moments without the use of likelihood functions, prior densities and Bayes’ Theorem. The posterior and predictive moments, based on a few relatively weak assumptions, are then used to obtain maximum entropy densities for parameters, realized error terms and future values of variables. Posterior means for parameters and realized error terms are shown to be equal to certain well known estimates and rationalized in terms of quadratic loss functions. Conditional maxent posterior densities for means and regression coefficients given scale parameters are in the normal form while scale parameters’ maxent densities are in the exponential form. Marginal densities for individual regression coefficients, realized error terms and future values are in the Laplace or double-exponential form with heavier tails than normal densities with the same means and variances. It is concluded that these results will be very useful, particularly when there is difficulty in formulating appropriate likelihood functions and prior densities needed in traditional maximum likelihood and Bayesian approaches.