Suchergebnisse

This article introduces a new R package (pspatreg) for the estimation of semiparametric spatial autoregressive models. pspatreg fits penalized spline semiparametric spatial autoregressive models via Restricted Maximum Likelihood or Maximum Likelihood. These models are very flexible since they make it possible to simultaneously control for spatial dependence, nonlinearities in the functional form, and spatio-temporal heterogeneity. The package also allows to estimate parametric spatial autoregressive models for both cross sectional and panel data (with fixed effects), thus avoiding the use of different libraries. The official demos, vignettes, and tutorials of the package are distributed either in CRAN or GitHub. This article illustrates the potential of the package by using an application to cross-sectional data.

The interaction between mathematicians, statisticians and econometricians working in actuarial sciences and finance is producing numerous meaningful scientific results. This volume introduces new ideas, in the form of four-page papers, presented at the international conference Mathematical and Statistical Methods for Actuarial Sciences and Finance (MAF), held at Universidad Carlos III de Madrid (Spain), 4th-6th April 2018. The book covers a wide variety of subjects in actuarial science and financial fields, all discussed in the context of the cooperation between the three quantitative approaches. The topics include: actuarial models; analysis of high frequency financial data; behavioural finance; carbon and green finance; credit risk methods and models; dynamic optimization in finance; financial econometrics; forecasting of dynamical actuarial and financial phenomena; fund performance evaluation; insurance portfolio risk analysis; interest rate models; longevity risk; machine learning and soft-computing in finance; management in insurance business; models and methods for financial time series analysis, models for financial derivatives; multivariate techniques for financial markets analysis; optimization in insurance; pricing; probability in actuarial sciences, insurance and finance; real world finance; risk management; solvency analysis; sovereign risk; static and dynamic portfolio selection and management; trading systems. This book is a valuable resource for academics, PhD students, practitioners, professionals and researchers, and is also of interest to other readers with quantitative background knowledge.

Mortality data provide valuable information for the study of the spatial distribution of mortality risk, in disciplines such as spatial epidemiology, medical demography, and public health. However, they are often available in an aggregated form over irregular geographical units, hindering the visualization of the underlying mortality risk and the detection of meaningful patterns. Also, it could be of interest to obtain mortality risk estimates on a finer spatial resolution, such that they can be linked with potential risk factors — in a posterior correlation analysis — that are usually measured in a different spatial resolution than mortality data. In this paper, we propose the use of the penalized composite link model and its representation as a mixed model to deal with these issues. This model takes into account the nature of mortality rates by incorporating the population size at the finest resolution, and allows the creation of mortality maps at a desirable scale, reducing the visual bias resulting from the spatial aggregation within original units. We illustrate our proposal with the analysis of several datasets related with deaths by respiratory diseases, cardiovascular diseases, and lung cancer. ; Acknowledgements: The first and second authors acknowledge financial support from the Spanish Ministry of Economy and Competitiveness grants MTM2011-28285-C02-02 and MTM2014-52184. The third author acknowledges financial support from the Basque Government through the BERC 2014-2017 program and by the Spanish Ministry of Economy and Competitiveness MINECO: BCAM Severo Ochoa excellence accreditation SEV-2013-0323.

Epidemiological data are frequently recorded at coarse spatio-temporal resolutions. The aggregation process is done for several reasons: to protect confidential patients' information, to compare with other datasets at a coarser resolution than the original, or to summarize data in a compact manner. However, we lose detailed patterns that follow the original data, which can be of interest for researchers and public health officials. In this paper we propose the use of the penalized composite link model (Eilers, 2007), together with its mixed model representation, to estimate the underlying trend behind grouped data at a finer spatio-temporal resolution. Also, this model allows the incorporation of fine-scale population into the estimation procedure. We assume the underlying trend is smooth across space and time. The mixed model representation enables the use of sophisticated algorithms such as the SAP algorithm of RodríguezÁlvarez et al. (2015) for fast estimation of the amount of smoothness. We illustrate our proposal with the analysis of data obtained during the largest outbreak of Q fever in the Netherlands. ; The first and the second authors acknowledge financial support from the Spanish Ministry of Economy and Competitiveness grants MTM2011-28285-C02-02 and MTM2014-52184-P. The third author acknowledges financial support from the Basque Government through the BERC 2014-2017 program and by the Spanish Ministry of Economy and Competitiveness MINECO: BCAM Severo Ochoa excellence accreditation SEV-2013-0323.

7 pages, no figures.-- Published online Sep 18, 2009. ; [Background] Intimate partner violence (IPV) is a public health problem with significant consequences on women's health. This study estimates the prevalence of intimate partner violence by type among Madrid's female population and assesses the association with socio-economic variables. ; [Methods] We conducted a cross-sectional study in 2004, 2136 women aged 18–70 years, living in the Madrid region with a partner or who had been in contact with an ex-partner in the previous year, were interviewed by telephone. The questionnaire used to measure past-year intimate partner violence, consisted of a Spanish translation of the psychological and sexual violence module of the French National Survey on Violence against Women, and the physical violence module of the Conflict Tactics Scale-1. To assess the association with socio-economic factors, logistic regression models were fitted. ; [Results] About 10.1% [confidence interval (CI) 8.9–11.5] of the women had suffered some type of IPV in the previous year. 8.6% (CI 7.4–9.8) experienced psychological violence, 2.4% (CI 1.8–3.1) physical violence and 1.1% (CI 0.68–1.6) sexual violence; the prevalence of psychological-only violence (non-physical/non-sexual) was 6.9% (CI 5.8–8.0). Factors associated with psychological-only violence were divorced or separated status and Group III (clerical workers; supervisors of manual workers) or V (unskilled manual workers) occupation. Unemployment and divorced or separated status were associated with physical violence. ; [Conclusions] Spanish women in our study, experienced past year partner violence at a similar level as in other industrialized countries. Unemployment and low occupational status are associated with physical and psychological-only violence, respectively. ; Funded by: Dirección General de Salud Pública; Agencia Laín Entralgo; Consejeria de Sanidad y Consumo, Comunidad de Madrid (Public Health General Directorate; Health and Consumers affairs Authority. Regional Government of the Madrid Region). ; Publicado