Methodology for the Calculation of Annualized Incremental Risks in Systems of Dams

In the past few years, the field of dam safety has approached risk informed methodologies throughout the world and several methodologies and programs are appearing to aid in the systematization of the calculations. The most common way of implementing these calculations is through the use of event trees, computing event probabilities, and incremental consequences. This methodology is flexible enough for several situations, but its generalization to the case of systems of several dams is complex and its implementation in a completely general calculation methodology presents some problems. Retaining the event tree framework, a new methodology is proposed to calculate incremental risks. The main advantage of this proposed methodology is the ease with which it can be applied to systems of several dams: with a single risk model that describes the complete system and with a single calculation the incremental risks of the system can be obtained, being able to allocate the risk of each dam and of each failure mode. The article shows how both methodologies are equivalent and also applies them to a case study.     

International job search: Mexicans in and out of the US

It is argued that migration from Mexico to the US and return migration are determined by international wage differentials and preferences for origin. We use a model of job search, savings and migration to show that job turnover is a crucial determinant of the migration process. We estimate this model by Simulated Method of Moments (SMM) and find that migration practically disappears, it goes down from 19 to 0.5%, had Mexico US arrival rates while employed. A lower decrease in migration, to 7%, occurs by an increase in Mexican wage offers. On the other hand, doubling migration costs reduces migration rates from 19 to 5%, while subsidizing return migration in $300 reduces migration rates only to 14%.     

Smoothing a Time Series by Segments of the Data Range

We consider the problem of estimating a trend with different amounts of smoothness for segments of a time series subjected to different variability regimes. We propose using an unobserved components model to consider the existence of at least two data segments. We first fix some desired percentages of smoothness for the trend segments and deduce the corresponding smoothing parameters involved. Once the size of each segment is chosen, the smoothing formulas here derived produce trend estimates for all segments with the desired smoothness as well as their corresponding estimated variances. Empirical examples from demography and economics illustrate our proposal.     

Influence analysis in multivariate linear general models

In this paper we obtain several influence measures for the multivariate linear general model through the approach proposed by Muñoz-Pichardo et al. (1995), which is based on the concept of conditional bias. An interesting charasteristic of this approach is that it does not require any distributional hypothesis. Appling the obtained results to the multivariate regression model, we obtain some measures proposed by other authors. Nevertheless, on the results obtained in this paper, we emphasize two aspects. First, they provide a theoretical foundation for measures proposed by other authors for the mul¬tivariate regression model. Second, they can be applied to any linear model that can be formulated as a particular case of the multivariate linear general model. In particular, we carry out an application to the multivariate analysis of covariance.     

The Ranking of the Regions With Regard to Their Sports Facilities to Improve Their Planning in Sport: The Case of Spain

Sports facility planning by the public authorities should be considered to be one of the main policies to have an indirect influence on the aim of ensuring a healthy population, as well as reducing its physical inactivity. Our research aims to study and compare the state of sports infrastructures in the regions of Spain, using a synthetic indicators. Our results should serve not only to detect the vast imparity between different regions, which has come about because of varying investment and management with regard to sports infrastructures, but also to redirect building planning and sports management policies onto the right path, and also give food for thought.     

Approximating probability density functions in hybrid Bayesian networks with mixtures of truncated exponentials

Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization and Monte Carlo methods for solving hybrid Bayesian networks. Any probability density function (PDF) can be approximated by an MTE potential, which can always be marginalized in closed form. This allows propagation to be done exactly using the Shenoy-Shafer architecture for computing marginals, with no restrictions on the construction of a join tree. This paper presents MTE potentials that approximate standard PDF’s and applications of these potentials for solving inference problems in hybrid Bayesian networks. These approximations will extend the types of inference problems that can be modelled with Bayesian networks, as demonstrated using three examples.     

Bivariate income distributions with lognormal conditionals

In this paper, the most general bivariate distribution with lognormal conditionals is fully characterized, using the methodology proposed by [3]. The properties of the new family are studied in detail, including marginal and conditional distributions, regression functions, dependence measures, moments and inequality measures. The new distribution is very broad, and contains as a particular case the classical bivariate lognormal distribution. Several subfamilies are studied and a generalization of the basic model is discussed. Finally, we present an empirical application. We estimate and compare the basic model proposed in the paper with a classical model, using data from the European Community Household Panel in different periods of time.