Ricerc@Sapienza

In the field of mortality, the Lee–Carter based approach can be considered the milestone
to forecast mortality rates among stochastic models. We could define a “Lee–Carter model family”
that embraces all developments of this model, including its first formulation (1992) that remains the
benchmark for comparing the performance of future models. In the Lee–Carter model, the kt parameter,
describing the mortality trend over time, plays an important role about the future mortality behavior.

Estimation of future mortality rates still plays a central role among life insurers in
pricing their products and managing longevity risk. In the literature on mortality modeling, a wide
number of stochastic models have been proposed, most of them forecasting future mortality
rates by extrapolating one or more latent factors. The abundance of proposed models shows that
forecasting future mortality from historical trends is non-trivial. Following the idea proposed in

The evolution of lifespan disparity is gaining a central role in mortality
literature. From the beginning of the new millennium, its evolution has led scholars to
give more emphasis to longevity and its relationship with causes-of-death evolution.
Following this line of research, we propose a novel model aiming to provide the
causes-of-death mortality surface, exploiting the relationship between mortality rates
by cause-of-death and lifespan variability. Taking advantage of this relationship, and

After the World War II, developed countries experienced a constant decline in mortality. As a result, life expectancy has never stopped increasing, despite an evident deceleration in developed countries, e.g. England, USA and Denmark. In this paper, we propose a new approach for forecasting life expectancy and lifespan disparity based on the recurrent neural networks with a long short-term memory.

The prices offered by the fixed-odd bookmakers in the tennis betting market are biased because of the favorite-longshot phenomenon. How to derive unbiased implied probabilities underlying the published odds is the focus of this study. This paper proposes a new normalization procedure that yields unbiased probabilities, regardless of the presence of heavy underdogs. In-sample, the proposed normalization has a superior forecasting ability than the other methods. Moreover, it enables betting strategies which produce superior re- turns than those obtained from the Bradley-Terry type model.

Recently, the interest of the academic literature on sports statistics has increased enormously. In such a framework, two of the most significant challenges are developing a model able to beat the
existing approaches and, within a betting market framework, guarantee superior returns than the set of competing specifications considered. This contribution attempts to achieve both these results, in

COVID-19 pandemic in Italy displayed a spatial distribution that made the tracking of its time course quite difficult. The most relevant anomaly was the marked spatial heterogeneity of COVID-19 diffusion. Lombardia region accounted for around 60% of fatal cases (while hosting 15% of Italian population). Moreover, 86% of fatalities concentrated in four Northern Italy regions. The ‘explosive’ outbreak of COVID-19 in Lombardia at the very beginning of pandemic fatally biased the R-like statistics routinely used to control the disease dynamics.

Aims: Exercise-derived parameters, specifically peak exercise oxygen uptake (peak VO 2 ) and minute ventilation/carbon dioxide relationship slope (VE/VCO 2 slope), have a pivotal prognostic value in heart failure (HF). It is unknown how the prognostic threshold of peak VO 2 and VE/VCO 2 slope has changed over the last 20 years in parallel with HF prognosis improvement. Methods and results: Data from 6083 HF patients (81% male, age 61 ± 13 years), enrolled in the MECKI score database between 1993 and 2015, were retrospectively analysed.

In this paper, we propose a quaternion widely linear approach for the forecasting of environmental data, in order to predict the air quality. Specifically, the proposed approach is based on a fusion of heterogeneous data via vector spaces. A quaternion data vector has been constructed by concatenating a set of four different measurements related to the air quality (such as CO, NO:2, SO:2, PM:10, an similar ones), then a Quaternion LMS (QLMS) algorithm is applied to predict next values from the previously ones.

There are many reasons that have led to the penetration of Renewable Energy Sources based (RES-based) generation plants in the Italian power systems, i.e. the sensitivity to environmental issues, the presence of massive economic incentives and the development of technology. This, with the evolution of electricity markets, introduced new problems in the operation of the transmission and distribution grids.