Emilio Gómez-Déniz

This study analyzes tourists’ stated willingness to pay (WTP) for a tourist tax using a beta-binomial model. WTP responses take discrete bounded values between €0 and €4 per night, which standard count models with unbounded support cannot accommodate. Our framework allows flexible dispersion patterns and incorporates covariates through a bounded logistic link. Using survey data from 35,377 tourists departing a mature sun-and-beach destination (2024Q2–2025Q1), we find that income, country of residence, accommodation type, length of stay, and satisfaction explain WTP heterogeneity. Out-of-sample validation on 17,010 observations from 2025Q2–Q3 shows well-calibrated probabilistic predictions, yielding a 35% Ranked Probability Score gain over a naïve baseline. Translating the fitted WTP distribution to a universe of 15.0 million tourists per year, a voluntary €2/night tax yields €131 million annually with 52% acceptance, while mandatory rates above €2 raise gross revenue at the cost of a rapidly rising burden on tourists beyond their stated tolerance.

Derived from the half hyperbolic secant distribution, new expressions and bounds for Catalan’s constant are presented. They are obtained by using expressions for the Lorenz curve, the Gini and Theil indices, convolutions, and a mixture of distributions, among other approaches. The new expressions are presented both in terms of integral (simple and double) representation and also as an interesting series representation. In addition, some integral representations of Apéry’s constant and Euler numbers are obtained.

This work aims to use a suitable regression model to study a count response random variable, namely, the number of citations of a research paper, that is affected by some explanatory variables. The count variable exhibits substantial variation, as the sample variance is larger than the sample mean; thus, the classical Poisson regression model seems not to be appropriate. We concentrate our attention on the negative binomial regression model, which allows the variance of each measurement to be a function of its predicted value. Nevertheless, the process of citations of papers may be divided into two parts. In the first stage, the paper has no citations, while the second part provides the intensity of the citations. A hurdle model for separating documents with citations and those without citations is considered. The dataset for empirical application consisted of 43,190 research papers in the Economics and Business field from 2014–2021, which were obtained from The Lens database. Citation counts and social attention scores for each article were gathered from the Altmetric database. The main findings indicate that both collaboration and funding have positive impacts on citation counts and reduce the likelihood of receiving zero citations. Open access (OA) via repositories (green OA) correlates with higher citation counts and a lower probability of zero citations. In contrast, OA via the publisher’s website without an explicit open license (bronze OA) is associated with higher citation counts but also with a higher probability of zero citations. In addition, open access in subscription-based journals (hybrid OA) increases citation counts, although the effect is modest. There are clear disciplinary differences, with the prestige of the journal playing a significant role in citation counts. Articles with lower expert ratings tend to be cited less frequently and are more likely to be cited zero times. Meanwhile, news and blog mentions boost citations and reduce the likelihood of receiving no citations, while policy mentions also enhance citation counts and significantly lower the risk of being cited zero times. In contrast, patent mentions have a negative impact on citations. The influence of social media varies: X/Twitter and Wikipedia mentions increase citations and reduce the likelihood of being uncited, whereas Facebook and video mentions negatively impact citation counts.

We combine two well-known statements of results in the statistical and mathematical literature, one related to symmetric continuous distributions and the other to the integration of functions, to obtain some new results regarding symmetric distributions and involving the value at risk and the tail value at risk, well-known tools used in actuarial and financial statistics, among others. Generalizations of the skew normal distribution in its univariate and multivariate versions obtained from one of the results are also shown, and a new method is proposed for generating families of skewed continuous distributions.

In the collective risk model and also in a compound excess-of-loss reinsurance frameworks, it is usual to assume that the risk parameters associated with the random variables, the number of claims, and the claim size are independent for mathematical convenience. Here, we assumed Poisson and Pareto distributions for these two random variables. This paper focuses on the prior and posterior (Bayesian) net premiums of the total claims amount, assuming some degree of dependence between the two risk profiles associated with these two random variables. Here, the degree of dependence was modeled using the Sarmanov-Lee family of distributions, a special type of copula, which allows us to study the impact of this assumption on the following year's total cost of claims when prior margins are assumed to have gamma and shifted Erlang distributions. The numerical applications show that a low degree of correlation between these variables leads to collective and net Bayes premiums that can be sensitive when the hypothesis of independence is broken. The dependence hypothesis has a more significant effect in the model when no layer is considered. We illustrate the methodology proposed with some real numerical examples.

In this paper a mixed Poisson regression model for count data is introduced. This model is derived by mixing the Poisson distribution with the one–parameter continuous exponential–inverse Gaussian distribution. The obtained probability mass function is over dispersed and unimodal with modal value located at zero. Estimation is performed by maximum likelihood. As an application, the demand for health services among people 65 and over is examined using this regression model since empirical evidence has suggested that the over-dispersion and a large portion of non–users are common features of medical care utilization data.

La mayoría de las empresas utilizan las redes sociales como canales de comunicación debido a los beneficios empresariales que pueden proporcionar. Este artículo se centra en el impacto de las redes sociales en una fundación española dedicada a la innovación y la difusión del conocimiento, y en cómo afectan a sus principales eventos y actividades. Examinamos los factores subyacentes a un retuit en Twitter o una compartición en Facebook con el fin de analizar la difusión de los eventos principales de esta fundación. Se realizaron comparaciones con tres modelos estadísticos (regresión estándar y regresión bayesiana con distribución a priori informativa y no informativa). Concluimos que la ventaja que ofrece la metodología bayesiana sobre la clásica se demuestra mediante la incorporación de información colateral, generalmente proporcionada por expertos, lo que permite refinar el modelo y obtener conclusiones que, de otro modo, no podrían ser identificadas. Esta conclusión puede tener implicaciones significativas para las empresas que utilizan redes sociales.