Local likelihood estimation of complex tail dependence structures, applied to U.S. precipitation extremes

To disentangle the complex non-stationary dependence structure of precipitation extremes over the entire contiguous U.S., we propose a flexible local approach based on factor copula models. Our sub-asymptotic spatial modeling framework yields non-trivial tail dependence structures, with a weakening dependence strength as events become more extreme, a feature commonly observed with precipitation data but not accounted for in classical asymptotic extreme-value models. To estimate the local extremal behavior, we fit the proposed model in small regional neighborhoods to high threshold exceedances, under the assumption of local stationarity, which allows us to gain in flexibility. Adopting a local censored likelihood approach, inference is made on a fine spatial grid, and local estimation is performed by taking advantage of distributed computing resources and the embarrassingly parallel nature of this estimation procedure. The local model is efficiently fitted at all grid points, and uncertainty is measured using a block bootstrap procedure. An extensive simulation study shows that our approach can adequately capture complex, non-stationary dependencies, while our study of U.S. winter precipitation data reveals interesting differences in local tail structures over space, which has important implications on regional risk assessment of extreme precipitation events

A spliced Gamma-Generalized Pareto model for short-term extreme wind speed probabilistic forecasting

Renewable sources of energy such as wind power have become a sustainable alternative to fossil fuel-based energy. However, the uncertainty and fluctuation of the wind speed derived from its intermittent nature bring a great threat to the wind power production stability, and to the wind turbines themselves. Lately, much work has been done on developing models to forecast average wind speed values, yet surprisingly little has focused on proposing models to accurately forecast extreme wind speeds, which can damage the turbines. In this work, we develop a flexible spliced Gamma-Generalized Pareto model to forecast extreme and non-extreme wind speeds simultaneously. Our model belongs to the class of latent Gaussian models, for which inference is conveniently performed based on the integrated nested Laplace approximation method. Considering a flexible additive regression structure, we propose two models for the latent linear predictor to capture the spatio-temporal dynamics of wind speeds. Our models are fast to fit and can describe both the bulk and the tail of the wind speed distribution while producing short-term extreme and non-extreme wind speed probabilistic forecasts.Comment: 25 page

Practical strategies for GEV-based regression models for extremes

The generalised extreme value (GEV) distribution is a three parameter family that describes the asymptotic behaviour of properly renormalised maxima of a sequence of independent and identically distributed random variables. If the shape parameter $\xi$ is zero, the GEV distribution has unbounded support, whereas if $\xi$ is positive, the limiting distribution is heavy-tailed with infinite upper endpoint but finite lower endpoint. In practical applications, we assume that the GEV family is a reasonable approximation for the distribution of maxima over blocks, and we fit it accordingly. This implies that GEV properties, such as finite lower endpoint in the case $\xi>0$, are inherited by the finite-sample maxima, which might not have bounded support. This is particularly problematic when predicting extreme observations based on multiple and interacting covariates. To tackle this usually overlooked issue, we propose a blended GEV distribution, which smoothly combines the left tail of a Gumbel distribution (GEV with $\xi=0$) with the right tail of a Fr\'echet distribution (GEV with $\xi>0$) and, therefore, has unbounded support. Using a Bayesian framework, we reparametrise the GEV distribution to offer a more natural interpretation of the (possibly covariate-dependent) model parameters. Independent priors over the new location and spread parameters induce a joint prior distribution for the original location and scale parameters. We introduce the concept of property-preserving penalised complexity (P$^3$C) priors and apply it to the shape parameter to preserve first and second moments. We illustrate our methods with an application to NO$_2$ pollution levels in California, which reveals the robustness of the bGEV distribution, as well as the suitability of the new parametrisation and the P$^3$C prior framework.Comment: 19 pages, 3 figure

Bayesian space-time gap filling for inference on extreme hot-spots: an application to Red Sea surface temperatures

We develop a method for probabilistic prediction of extreme value hot-spots in a spatio-temporal framework, tailored to big datasets containing important gaps. In this setting, direct calculation of summaries from data, such as the minimum over a space-time domain, is not possible. To obtain predictive distributions for such cluster summaries, we propose a two-step approach. We first model marginal distributions with a focus on accurate modeling of the right tail and then, after transforming the data to a standard Gaussian scale, we estimate a Gaussian space-time dependence model defined locally in the time domain for the space-time subregions where we want to predict. In the first step, we detrend the mean and standard deviation of the data and fit a spatially resolved generalized Pareto distribution to apply a correction of the upper tail. To ensure spatial smoothness of the estimated trends, we either pool data using nearest-neighbor techniques, or apply generalized additive regression modeling. To cope with high space-time resolution of data, the local Gaussian models use a Markov representation of the Mat\'ern correlation function based on the stochastic partial differential equations (SPDE) approach. In the second step, they are fitted in a Bayesian framework through the integrated nested Laplace approximation implemented in R-INLA. Finally, posterior samples are generated to provide statistical inferences through Monte-Carlo estimation. Motivated by the 2019 Extreme Value Analysis data challenge, we illustrate our approach to predict the distribution of local space-time minima in anomalies of Red Sea surface temperatures, using a gridded dataset (11315 days, 16703 pixels) with artificially generated gaps. In particular, we show the improved performance of our two-step approach over a purely Gaussian model without tail transformations

Modelling short-term precipitation extremes with the blended generalised extreme value distribution

The yearly maxima of short-term precipitation are modelled to produce improved spatial maps of return levels over the south of Norway. The newly proposed blended generalised extreme value (bGEV) distribution is used as a substitute for the more standard generalised extreme value (GEV) distribution in order to simplify inference. Yearly precipitation maxima are modelled using a Bayesian hierarchical model with a latent Gaussian field. Fast inference is performed using the framework of integrated nested Laplace approximations (INLA). Inference is made less wasteful with a two-step procedure that performs separate modelling of the scale parameter of the bGEV distribution using peaks over threshold data. Our model provides good estimates for large return levels of short-term precipitation, and it outperforms standard block maxima models.Comment: 20 pages, 4 figures; reference adde

A spliced Gamma-Generalized Pareto model for short-term extreme wind speed probabilistic forecasting

Renewable sources of energy such as wind power have become a sustainable alternative to fossil fuel-based energy. However, the uncertainty and fluctuation of the wind speed derived from its intermittent nature bring a great threat to the wind power production stability, and to the wind turbines themselves. Lately, much work has been done on developing models to forecast average wind speed values, yet surprisingly little has focused on proposing models to accurately forecast extreme wind speeds, which can damage the turbines. In this work, we develop a flexible spliced Gamma-Generalized Pareto model to forecast extreme and non-extreme wind speeds simultaneously. Our model belongs to the class of latent Gaussian models, for which inference is conveniently performed based on the integrated nested Laplace approximation method. Considering a flexible additive regression structure, we propose two models for the latent linear predictor to capture the spatio-temporal dynamics of wind speeds. Our models are fast to fit and can describe both the bulk and the tail of the wind speed distribution while producing short-term extreme and non-extreme wind speed probabilistic forecasts. Supplementary materials accompanying this paper appear online

Aplicación de un programa de actividad física en deportes de combate para la mejora de las capacidades físicas y de convivencia en aficionados de fútbol bogotano

58 páginas : gráficasEsta investigación busca Diseñar un programa de actividad física orientado en deportes de combate, para los aficionados de fútbol bogotanos, aplicar el programa de actividad fisica para definir el acambio de las aptitudes físicas de fuerza y velocidad desarrolladas en el programa de actividad física en deportes de combate y evaluar los resultados obtenidos en los cambios de la agresividad y convivencia del grupo de aficionados a través de la aplicación del programa de actividad física y comparalos con estudios y publicaciones realizadas frente a los beneficios del entrenamiento para el comportamiento en aficionados de fútbol bogotanoIncluye bibliografíaPregradoProfesional en Ciencias del Deport

Time-varying extreme value dependence with application to leading European stock markets

Extremal dependence between international stock markets is of particular interest in today's global financial landscape. However, previous studies have shown this dependence is not necessarily stationary over time. We concern ourselves with modeling extreme value dependence when that dependence is changing over time, or other suitable covariate. Working within a framework of asymptotic dependence, we introduce a regression model for the angular density of a bivariate extreme value distribution that allows us to assess how extremal dependence evolves over a covariate. We apply the proposed model to assess the dynamics governing extremal dependence of some leading European stock markets over the last three decades, and find evidence of an increase in extremal dependence over recent years.Comment: 23 page

Impacts of the Inclusion of Distributed Generation on Congestion of Distribution Networks and in the Islanding Operation Capability

The growing demand for electricity in the world has led to power systems having to constantly increase their generation capacity and expand their transmission and distribution systems. Consequently, distributed generation has positioned as a technology able to integrate generation close to consumption centers, freeing up capacity in the transport systems, which can be translated into a deferral of investments in network expansion. Therefore, this paper analyzes the impact of the inclusion of distributed generation in the congestion of a typical distribution network and evaluates the potential of providing the island operation capability ancillary service in a section of the system to identify the possible challenges and benefits that the development of this technical support service could have in typical Colombian distribution networks.Universidad Tecnológica de Bolíva

Diseño de un plan de mejoramiento de los procesos logísticos de control de acceso, en el centro de procesamiento de fluidos de Ecopetrol floreña Yopal

Imágenes, tablas y graficas.En el siguiente trabajo se pretende diseñar de un plan de mejoramiento de los procesos logísticos de control de acceso, en el centro de procesamiento de fluidos de Ecopetrol Floreña Yopal, teniendo en cuenta la problemática de los cambios que se deben dar ante situaciones transitorias, teniendo en cuenta los retos post covid-19. Es importante mencionar que para la realización de la misma se sostiene una metodología de carácter mixto por el origen de variables que son estructuradas bajo cálculos estadísticos y tabulación a través de Excel y SPSS; y categorías a través del método AMFE, ejecutando 4 fases en las cuales se busca caracterizar los procesos logísticos, realizando un diagnóstico de ellos, por medio de las herramientas de calidad obteniendo resultados como estructura base de conclusiones y decisiones que se propondrán como mejora de los procesos logísticos de control de acceso incremento de la eficiencia operativa, mejora de la seguridad, optimización de recursos, cumplimiento normativo, a través de la apropiación de conocimiento durante el programa de tecnología en logística industrial en la UNAD por medio de un proyecto aplicado.The following work aims to design an improvement plan for the logistic processes of access control in the fluid processing center of Ecopetrol Floreña Yopal, taking into account the challenges posed by transitional situations and the post-COVID-19 era. It is important to mention that a mixed methodology is employed for this study, as it incorporates both structured variables analyzed through statistical calculations and tabulation using Excel and SPSS, as well as categories assessed through the Failure Mode and Effects Analysis (FMEA) method. The study is conducted in four phases, which involve characterizing the logistic processes and diagnosing them through quality tools to obtain results that serve as the basis for conclusions and proposed decisions for the improvement of the logistic processes of access control, aiming to enhance operational efficiency, improve security, optimize resources, and ensure compliance with regulations. These improvements are achieved through the acquisition of knowledge during the industrial logistics technology program at UNAD through an applied project