Default prior choices fixing Zellner's g are predominant in the Bayesian Model Averaging literature, but tend to concentrate posterior mass on a tiny set of models. The paper demonstrates this supermodel effect and proposes to address it by a hyper-g prior, whose data-dependent shrinkage adapts...

This work presents a class of models in asset pricing, whose underlying has dynamics of Affine jump diffusion type. We first present L´evy processes with their properties. We then introduce Affine jump diffusion processes whichare basically a particular class of L´evy processes. Our motivation...

Multivariate time series analysis became popular in the early 1950s when the need to analyse time series simultaneously arose in the field of economics. This study provides an overview of some of the aspects of multivariate time series analysis in the case of stationarity. The VARMA (vector...

Resumen:Este artículo se refiere a la aplicación de la curva logística, que es un instrumento matemático usado por biólogos y demografistas para describir el crecimiento de poblaciones animales y de poblaciones humanas, así como para formular pronósticos futuros, a los censos de la ciudad...

The causes of financial crises are multiple but the models of financial crises revolve around four generational models. In this paper, the authors analyzed these models and highlighted the fact that each model was adapted to specific situations to explain the financial crises faced rather than...

Dust elevated into the atmosphere by dust storms has numerous environmental consequences. These include contributing to climate change; modifying local weather conditions; producing chemical and biological changes in the oceans; and affecting soil formation, surface water, groundwater quality,...

We study model selection strategies based on penalized empirical loss minimization. We point out a tight relationship between error estimation and data-based complexity penalization: any good error estimate may be converted into a data-based penalty function and the performance of the estimate...

Linear programming is a tool that has been successfully applied to various problems across many different industries and businesses. However, it appears that casino operators may have overlooked this useful and proven method. At most casino properties the bulk of gaming revenues are derived from...

Gradient-based approaches to direct policy search in reinforcement learning have received much recent attention as a means to solve problems of partial observability and to avoid some of the problems associated with policy degradation in value-function methods. In this paper we introduce GPOMDP,...

Actuaries and demographers have a long tradition of utilising collateral data to improve mortality estimates. Three main approaches have been used to accomplish the improvement- mortality laws, model life tables, and relational methods. The present paper introduces a regression model that...