An Extreme-Value Model for Predicting the Results of Horse Races

1984 ◽  
Vol 33 (2) ◽  
pp. 125 ◽  
Author(s):  
R. J. Henery
Keyword(s):  
Extreme Value ◽  
Extreme Value Model ◽  
Value Model

A Bivariate Extreme Value Model for Estimating Crash Frequency by Severity using Traffic Conflicts

2021 ◽  
pp. 100180
Author(s):  
Ashutosh Arun ◽  
Md. Mazharul Haque ◽  
Ashish Bhaskar ◽  
Simon Washington ◽  
Tarek Sayed
Keyword(s):  
Extreme Value ◽  
Crash Frequency ◽  
Traffic Conflicts ◽  
Extreme Value Model ◽  
Value Model

An Extreme Value Model for U.S. Hail Size

2017 ◽  
Vol 145 (11) ◽  
pp. 4501-4519 ◽  
Author(s):  
John T. Allen ◽  
Michael K. Tippett ◽  
Yasir Kaheil ◽  
Adam H. Sobel ◽  
Chiara Lepore ◽  
...  
Keyword(s):  
Gumbel Distribution ◽  
The United States ◽  
Value Theory ◽  
Extreme Value ◽  
Extreme Value Distributions ◽  
The Past ◽  
Return Levels ◽  
Data Limitations ◽  
Extreme Value Model ◽  
Value Model

The spatial distribution of return intervals for U.S. hail size is explored within the framework of extreme value theory using observations from the period 1979–2013. The center of the continent has experienced hail in excess of 5 in. (127 mm) during the past 30 yr, whereas hail in excess of 1 in. (25 mm) is more common in other regions, including the West Coast. Observed hail sizes show heavy quantization toward fixed-diameter reference objects and are influenced by spatial and temporal biases similar to those noted for hail occurrence. Recorded hail diameters have been growing in recent decades because of improved reporting. These data limitations motivate exploration of extreme value distributions to represent the return periods for various hail diameters. The parameters of a Gumbel distribution are fit to dithered observed annual maxima on a national 1° × 1° grid at locations with sufficient records. Gridded and kernel-smoothed return sizes and quantiles up to the 200-yr return period are determined for the fitted Gumbel distribution. These results are used to illustrate return levels for hail greater than a given size for at least one location within each 1° × 1° grid box for the United States.

Towards a reproducible snow load map – an example for Austria

2021 ◽  
Author(s):  
Harald Schellander ◽  
Michael Winkler ◽  
Tobias Hell
Keyword(s):  
Additive Model ◽  
Penalized Regression ◽  
Extreme Value ◽  
Ease Of Use ◽  
Return Level ◽  
Snow Load ◽  
Snow Model ◽  
Penalized Regression Splines ◽  
Extreme Value Model ◽  
Value Model

<p>The European Committee for Standardization provides coarse rules for the estimation of snow load maps for structural design. European countries can apply their own methodologies, resulting in inconsistencies for the 50-year return level of snow load at national borders. Commonly used approaches base on more or less sophisticated interpolation of snow depths with a subsequent assignment of snow density, or spatial extreme value interpolation of snow load measurements.  </p><p>We propose a novel methodology for Austria, where snow load observations are not available. It is based on (1) modeling yearly snow load maxima with the specially developed ∆SNOW model, and (2) a generalized additive model, where explaining covariates and their combinations are represented by penalized regression splines, fitted to such derived snow load series. Results show an RMSE of 0.7 kN/m<sup>2</sup>, and a BIAS of -0.2 kN/m<sup>2</sup> over all altitudes, thereby outperforming a smooth spatial extreme value model and the actual Austrian standard, when compared to locally estimated, “quasi-observed “ 50-year snow load maxima at 870 stations in and tightly around Austria.</p><p>The new approach requires no zoning and provides a reproducible and transparent approach. Due to the relatively ease of use and snow depth measurements as single prerequisite, the method is applicable in other countries as well. Negative BIASes, that significantly underestimate 50-year snow loads at a small number of stations, are the only objective problem that has to be solved before the new map can be proposed as a successor of the actual Austrian snow load map.</p>

scholarly journals Evaluation of classical spatial-analysis schemes of extreme rainfall

2012 ◽  
Vol 12 (11) ◽  
pp. 3229-3240 ◽  
Author(s):  
D. Ceresetti ◽  
E. Ursu ◽  
J. Carreau ◽  
S. Anquetin ◽  
J. D. Creutin ◽  
...  
Keyword(s):  
Cross Validation ◽  
Extreme Rainfall ◽  
Value Theory ◽  
Extreme Value ◽  
Optimal Interpolation ◽  
Peaks Over Threshold ◽  
Model Block ◽  
Block Maxima ◽  
Extreme Value Model ◽  
Value Model

Abstract. Extreme rainfall is classically estimated using raingauge data at raingauge locations. An important related issue is to assess return levels of extreme rainfall at ungauged sites. Classical methods consist in interpolating extreme-value models. In this paper, such methods are referred to as regionalization schemes. Our goal is to evaluate three classical regionalization schemes. Each scheme consists of an extreme-value model (block maxima, peaks over threshold) taken from extreme-value theory plus a method to interpolate the parameters of the statistical model throughout the Cévennes-Vivarais region. From the interpolated parameters, the 100-yr quantile level can be estimated over this whole region. A reference regionalization scheme is made of the couple block maxima/kriging, where kriging is an optimal interpolation method. The two other schemes differ from the reference by replacing either the extreme-value model block maxima by peaks over threshold or kriging by a neural network interpolation procedure. Hyper-parameters are selected by cross-validation and the three regionalization schemes are compared by double cross-validation. Our evaluation criteria are based on the ability to interpolate the 100-yr return level both in terms of precision and spatial distribution. It turns out that the best results are obtained by the regionalization scheme combining the peaks-over-threshold method with kriging.

A generalized Pareto distribution–based extreme value model of thermal gradients in a long-span bridge combining parameter updating

2016 ◽  
Vol 20 (2) ◽  
pp. 202-213 ◽  
Author(s):  
Guang-Dong Zhou ◽  
Ting-Hua Yi ◽  
Bin Chen ◽  
Huan Zhang
Keyword(s):  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Extreme Value ◽  
Thermal Gradients ◽  
Long Span Bridges ◽  
Long Span ◽  
Generalized Pareto ◽  
Long Span Bridge ◽  
Extreme Value Model ◽  
Value Model

Estimating extreme value models with high reliability for thermal gradients is a significant task that must be completed before reasonable thermal loads and possible thermal stress in long-span bridges are evaluated. In this article, a generalized Pareto distribution–based extreme value model combining parameter updating has been developed to describe the statistical characteristics of thermal gradients in a long-span bridge. The procedure of excluding correlation and the approach of selecting a proper threshold are suggested to prepare samples for generalized Pareto distribution estimation. A Bayesian estimation, which has the capability of updating model parameters by fusing prior information and incoming monitoring data, is proposed to fit the generalized Pareto distribution–based model. Furthermore, the Gibbs sampling, which is a Markov chain Monte Carlo algorithm, is adopted to derive the Bayesian posterior distribution. Finally, the proposed method is applied to the field monitoring data of thermal gradients in the Jiubao Bridge. The extreme value models of thermal gradients for the Jiubao Bridge are established, and the extreme thermal gradients with different return periods are extrapolated. The results indicate that the generalized Pareto distribution–based extreme value model has a strong ability to represent the statistical features of thermal gradients for the Jiubao Bridge, and the Bayesian estimation combining parameter updating provides high-precision generalized Pareto distribution–based models for predicting extreme thermal gradients. The predicted extreme thermal gradients are expected to evaluate and design long-span bridges.

Sign in / Sign up

Export Citation Format

Share Document