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Models for exceedances over high thresholds
We discuss the analysis of the extremes of data by modelling the sizes and occurrence of exceedances over high thresholds. The natural distribution for such exceedances, the generalized ParetoExpand
Maximum likelihood estimation in a class of nonregular cases
SUMMARY We consider maximum likelihood estimation of the parameters of a probability density which is zero for x 2, the information matrix is finite and the classical asymptotic properties continueExpand
Estimating tails of probability distributions
On propose un nouvel estimateur pour un indice de variation reguliere et on montre qu'il est souvent plus performant que l'estimateur de Hill (1975)
Max-stable processes arise from an infinite-dimensional generalisation of extreme value theory. They form a natural class of processes when sample maxima are observed at each site of a spatialExpand
A Comparison of Maximum Likelihood and Bayesian Estimators for the Three‐Parameter Weibull Distribution
Maximum likelihood and Bayesian estimators are developed and compared for the three‐parameter Weibull distribution. For the data analysed in the paper, the two sets of estimators are found to be veryExpand
Quantifying Uncertainty in Projections of Regional Climate Change: A Bayesian Approach to the Analysis of Multimodel Ensembles
Abstract A Bayesian statistical model is proposed that combines information from a multimodel ensemble of atmosphere–ocean general circulation models (AOGCMs) and observations to determineExpand
Extreme Value Analysis of Environmental Time Series: An Application to Trend Detection in Ground-Level Ozone
Several methods of analyzing extreme values are now known, most based on the extreme value limit distributions or related families. This paper reviews these techniques and proposes some extensionsExpand
Bayesian Modeling of Uncertainty in Ensembles of Climate Models
Projections of future climate change caused by increasing greenhouse gases depend critically on numerical climate models coupling the ocean and atmosphere (global climate models [GCMs]). However,Expand