https://hdl.handle.net/2072/478941 2026-04-04T14:34:35Z https://hdl.handle.net/2445/97866 Is the educational health gap increasing for women? Results from Catalonia (Spain) Solé i Auró, Aïda; Alcañiz, Manuela Health expectancies vary worldwide according to socioeconomic status (SES). The lower SES usually show health disadvantage and the higher SES a health advantage compared to the average. The educational level of individuals is strongly linked to their SES. 2016-04-26T07:56:15Z https://hdl.handle.net/2445/97862 Mortality and longevity risks in the United Kingdom: Dynamic factor models and copula-functions Chuliá Soler, Helena; Guillén, Montserrat; Uribe Gil, Jorge Mario We present a methodology to forecast mortality rates and estimate longevity and mortality risks. The methodology uses Generalized Dynamic Factor Models fitted over the differences of the log-mortality rates. We compare prediction performance with models previously proposed in the literature, such as the traditional Static Factor Model fitted over the level of log-mortality rates. We also construct risks measures by the means of vine-copula simulations, taking into account the dependence between the idiosyncratic components of the mortality rates. The methodology is implemented to project the mortality rates of the United Kingdom, for which we consider a portfolio and study longevity and mortality risks. 2016-04-26T07:11:46Z https://hdl.handle.net/2445/98448 Accounting for severity of risk when pricing insurance products Alemany Leira, Ramon; Bolancé Losilla, Catalina; Guillén, Montserrat We design a system for improving the calculation of the price to be charged for an insurance product. Standard pricing techniques generally take into account the expected severity of potential losses. However, the severity of a loss can be extremely high and the risk of a severe loss is not homogeneous for all policy holders. We argue that risk loadings should be based on risk evaluations that avoid too many model assumptions. We apply a nonparametric method and illustrate our contribution with a real problem in the area of motor insurance. 2016-05-09T15:01:05Z https://hdl.handle.net/2445/98449 Optimal personalized treatment rules for marketing interventions: A review of methods, a new proposal, and an insurance case study Guelman, Leo; Guillén, Montserrat; Pérez Marín, Ana María In many important settings, subjects can show signi cant heterogeneity in response to a stimulus or treatment". For instance, a treatment that works for the overall population might be highly ine ective, or even harmful, for a subgroup of subjects with speci c characteristics. Similarly, a new treatment may not be better than an existing treatment in the overall population, but there is likely a subgroup of subjects who would bene t from it. The notion that "one size may not fit all" is becoming increasingly recognized in a wide variety of elds, ranging from economics to medicine. This has drawn signi cant attention to personalize the choice of treatment, so it is optimal for each individual. An optimal personalized treatment is the one that maximizes the probability of a desirable outcome. We call the task of learning the optimal personalized treatment "personalized treatment learning". From the statistical learning perspective, this problem imposes some challenges, primarily because the optimal treatment is unknown on a given training set. A number of statistical methods have been proposed recently to tackle this problem. 2016-05-09T15:12:00Z