Extreme Value Model (extreme + value_model)

Distribution by Scientific Domains

Selected Abstracts

A Bayesian hierarchical extreme value model for lichenometry

Daniel Cooley
Abstract Currently, there is a tremendous scientific research effort in the area of climate change. In this paper, our motivation is to improve the understanding of historical climatic events such as the Little Ice Age (LIA), a period of relatively cold weather around 1450,1850 AD. Although the LIA is well documented in Europe, its extent and timing are not known in areas of the globe where climatological records were not kept during this period. To study the climate, which predates historical records, proxy climate records must be used. A proxy record for the timing of climatic cooling events are the ages of the moraines left behind by glacial advances. Unfortunately, to determine the ages of these moraines in alpine environments there is little material available but lichens. Hence, lichenometry was developed to determine the ages of glacial landforms by using lichen measurements. To our knowledge, this article provides the first attempt at deriving a comprehensive statistical model for lichenometry. Our model foundation is based on extreme value theory because only the largest lichens are measured in lichenometry studies. This application is novel to extreme value theory because the quantities of interest (the ages of climatic events) are not the measured quantities (lichen diameters), i.e., it is a inverse problem. We model the lichen measurements with the generalized extreme value (GEV) distribution, upon which a Bayesian hierarchical model is built. The hierarchical model enables estimation of the hidden covariate ages of the moraines. The model also allows for pooling of data from different locations and evaluation of spatial differences in lichen growth. Parameter inference is obtained using a straightforward Markov Chain Monte Carlo method. Our procedure is applied to data gathered from the Cordillera Real region in Bolivia. Copyright © 2006 John Wiley & Sons, Ltd. [source]

Dynamical versus statistical downscaling methods for ocean wave heights

Xiaolan L. Wang
Abstract In this study, dynamical and statistical downscaling methods for estimating seasonal statistics of significant wave heights (SWH) were intercompared, with the downscaling results being evaluated against the ERA40 wave data in terms of climatological characteristics and interannual variability. It was also shown that biases in climate-model-simulated climate and variability of the atmospheric circulation (or predictors in general) can result in large biases in the estimated climate and variability of SWH (or the predictand in general), and that such biases can be effectively diminished by using standardized predictor quantities in statistical downscaling models. In dynamical downscaling, however, model variability biases remain to be dealt with, whereas the effects of model climate biases can be reduced to some extent by replacing the climate-model-simulated wind climate with the observed one. Therefore, the dynamical approach was found to be not as good as the statistical methods in terms of reproducing the observed climate and interannual variability of the predictand, although it bears substantial similarity to the statistical methods in terms of projected possible future changes. Also, it was shown that the observed interannual variability of seasonal statistics (including extremes) can be better reproduced by using 12-hourly, rather than seasonal, data in statistical downscaling. This stresses the importance of availability of higher-resolution data from climate model outputs. Nevertheless, a non-stationary extreme value model with covariates was found to be the best in reproducing the observed climate of extremes. All the statistical downscaling methods and the intercomparison results are applicable to other climate variables (not limited to ocean wave heights). Copyright © 2009 Crown in the right of Canada. Published by John Wiley & Sons, Ltd. [source]

Modeling tropical cyclone intensity with quantile regression

Thomas H. Jagger
Abstract Wind speeds from tropical cyclones (TCs) occurring near the USA are modeled with climate variables (covariates) using quantile regression. The influences of Atlantic sea-surface temperature (SST), the Pacific El Niño, and the North Atlantic oscillation (NAO) on near-coastal TC intensity are in the direction anticipated from previous studies using Poisson regression on cyclone counts and are, in general, strongest for higher intensity quantiles. The influence of solar activity, a new covariate, peaks near the median intensity level, but the relationship switches sign for the highest quantiles. An advantage of the quantile regression approach over a traditional parametric extreme value model is that it allows easier interpretation of model coefficients (parameters) with respect to changes to the covariates since coefficients vary as a function of quantile. It is proven mathematically that parameters of the Generalized Pareto Distribution (GPD) for extreme events can be used to estimate regression coefficients for the extreme quantiles. The mathematical relationship is demonstrated empirically using the subset of TC intensities exceeding 96 kt (49 m/s). Copyright © 2008 Royal Meteorological Society [source]

Risk preference and employment contract type

Sarah Brown
Summary., We explore the possibility that a systematic relationship exists between employment within a particular type of contract and risk preference. We exploit a set of proxies for risk preference, whereby some of the proxies capture risk loving behaviour (expenditure on gambling, smoking and alcohol) whereas others capture risk averse behaviour (expenditure on life and contents insurance, and unearned income). The empirical analysis, based on pooled cross-section data from the UK Family Expenditure Survey, 1997,2000, provides evidence of a systematic relationship between employment contract type and risk preference, with, for example, self-employed workers being more or less likely to engage in the consumption of ,risky' or financial security products respectively. The results are based on the ordered generalized extreme value model, a relatively infrequently used discrete choice model, which allows for ordering and correlation in the alternatives observed. [source]