Noticias

Likelihood-based inference for power distributions
Ponente/Speaker: Héctor W. Gómez (Universidad de Antofagasta (Chile)).
Email:  
Fecha/Date: 20-04-2018.
Lugar/Venue: Aula de informática D3.6.
Hora/Time: 12:00.

Abstract:

This paper considers likelihood-based inference for the family of power distributions. Widely applicable
results are presented which can be used to conduct inference for all three parameters of the general
location-scale extension of the family. More specic results are given for the special case of the power
normal model. The analysis of a large data set, formed from density measurements for a certain type of
pollen, illustrates the application of the family and the results for likelihood-based inference. Through-
out, comparisons are made with analogous results for the direct parametrisation of the skew-normal
distribution.

Key Contact: E. Gómez-Déniz.

+info: http://www.dmc.ulpgc.es/seminarios/1313-likelihood-based-inference-for-power-distributions.html

Work-in-Progress Seminars

daea.ulpgc.es & dmc.ulpgc.es

Título:  Análisis de las Zonzas Económicas Especiales (ZEE) y su influencia en la economía de España

Ponente: María Ceballos (ULPGC)

Lugar: Seminario D-2.1

Fecha: Lunes 28 de mayo de 2018. 12:00

Duración: 20 minutos exposición + 10 minutos preguntas.

Observaciones: Café + refrigerio. Horario estricto. Para cualquier cuestión, contacte con .

Título/Title: Model Averaging techniques with economic applications: a Bayesian perspective
Ponente/Speaker: Gonzalo García-Donato (Universidad de Castilla-La Mancha).
Email:  
Web:  https://scholar.google.es/citations?hl=es&user=nrYDVBQAAAAJ&view_op=list_works&sortby=pubdate
Fecha/Date: 16-03-2018.
Lugar/Venue: Aula de Informática D3.6.
Hora/Time: 12:00.

Abstract: A common situation in applied disciplines is that there is no a universally accepted theory leading to the construction of a single statistical model. More on the contrary, several different statistical models may be built based on the consideration of alternative sensible theories about the problem under study. This context is normally handled choosing one of these competing models then producing inferences conditionally on this (now fixed) model. Nevertheless, this approach obviates the uncertainty regarding which is the true model leading to an underevaluation of the variability and potentially to incorrect inferences. The statistical techniques that explicitly consider this source of extra variability through weighting inferences over the different models are called Model Averaging (MA). These procedures have received great interest in social sciences and in economics in particular and will be the subject of this talk. We will review the basic aspects about the usage of MA with special focus on the Bayesian approach, which arguably is the natural way to approach the problem. The emphasis is placed on applicability and examples of applications in economics will be given jointly with a review of the software that can be used to approach a MA problem.

El trabajo introduce un modelo estadístico bivariado que permite estudiar los determinantes del número de tarjetas amarillas y rojas mostradas en una competición futbolística, como la liga nacional de fútbol, la copa del mundo o la UEFA Champions League. El modelo se ha aplicado a la competición de la liga española de fútbol en la temporada 2013-14, analizando los efectos parciales y totales, tanto para el equipo que juega como local como para el que actúa como visitante.

Descarga del artículo: 

http://siba-ese.unile.it/index.php/ejasa/article/view/15911/15592

Dossier de prensa: http://www2.ulpgc.es/hege/almacen/download/7125/7125952/viernes_19_de_enero.pdf

Entrevista “El Espejo Canario” (a partir del minuto 3:39:00):

http://www.elespejocanario.es/audios/ampliar/9571/el-espejo-canario-viernes-19-de-enero-de-2018

+Info: http://www.ulpgc.es/noticia/investigadores-ulpgc-analizan-factores-que-afectan-al-numero-tarjetas-amarillas-y-rojas