1,228 research outputs found
Local likelihood estimation of complex tail dependence structures, applied to U.S. precipitation extremes
Get PDFTo 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
Get PDFRenewable 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
Get PDFThe 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 is zero, the GEV distribution has unbounded support,
whereas if 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 , 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 ) with the right tail of a Fr\'echet
distribution (GEV with ) 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 (PC) priors and
apply it to the shape parameter to preserve first and second moments. We
illustrate our methods with an application to NO pollution levels in
California, which reveals the robustness of the bGEV distribution, as well as
the suitability of the new parametrisation and the PC 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
Full text linkWe 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
Get PDFThe 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 wee exploration of techniques for risk assessments of extreme events
Get PDFAssessing the behaviour of extreme events in univariate and multivariate settings entails many challenges, from the need to capture different sources of non-stationarity to adequately extrapolate into the tail of the distribution and compute probabilities of extreme events associated with high-dimensional vectors. Motivated by these common issues, we use a combination of methods from extreme-value theory, dimensionality reduction, non-parametric statistics, copula theory, and bootstrap model averaging to provide estimates of risk measures associated with environmental extremes. The work is tailored to the four data challenges presented in the EVA (2023) Conference Data Challenge competition, and the methods introduced here represent the approach taken by the Wee Extremes group
A spliced Gamma-Generalized Pareto model for short-term extreme wind speed probabilistic forecasting
Get PDFRenewable 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
Impacts of the Inclusion of Distributed Generation on Congestion of Distribution Networks and in the Islanding Operation Capability
Get PDFThe 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
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
Get PDF58 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
Get PDFExtremal 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
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