Mathieu Ribatet

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The areal modeling of the extremes of a natural process such as rainfall or temperature is important in environmental statistics; for example, understanding extreme areal rainfall is crucial in flood protection. This article reviews recent progress in the statistical modeling of spatial extremes, starting with sketches of the necessary elements of extreme(More)
Composite likelihoods are increasingly used in applications where the full likelihood is analytically unknown or computationally prohibitive. Although some frequentist properties of the maximum composite likelihood estimator are akin to those of the maximum likelihood estimator, Bayesian inference based on composite likelihoods is in its early stages. This(More)
1.1 Two simulations of the Smith model with different Σ matrices. Left panel: σ 11 = σ 22 = 9/8 and σ 12 = 0. Right panel: σ 11 = σ 22 = 9/8 and σ 12 = 1. The max-stable processes are transformed to unit Gumbel margins for viewing purposes.. 6 1.2 Plots of the Whittle–Matérn, the powered exponential, the Cauchy and the Bessel correlation functions-from left(More)
The global sensitivity analysis, used to quantify the influence of uncertain input parameters on the response variability of a numerical model, is applicable to deterministic computer code (for which the same set of input parameters gives always the same output value). This paper proposes a new global sensitivity analysis method for stochastic computer code(More)
Flood Frequency Analysis is usually based on the fitting of an extreme value distribution to the series of local streamflow. However, when the local data series is short, frequency analysis results become unreliable. Regional frequency analysis is a convenient way to reduce the estimation uncertainty. In this work, we propose a regional Bayesian model for(More)
The global sensitivity analysis method, used to quantify the influence of uncertain input variables on the response variability of a numerical model, is applicable to deterministic computer code (for which the same set of input variables gives always the same output value). This paper proposes a global sensitivity analysis methodology for stochastic(More)
Global sensitivity analysis is used to quantify the influence of uncertain input parameters on the response variability of a numerical model. The common quantitative methods are appropriate with computer codes having scalar input variables. This paper aims at illustrating different variance-based sensitivity analysis techniques, based on the so-called(More)
Flood quantile estimation is of great importance for several types of engineering studies and policy decisions. However, practitioners must often deal with the limited availability of data and with short length observation series. Thus, the information must be used optimally. During the last decades, to make better use of available data, inferential(More)
Abstract 10 Regional flood frequency analysis is a convenient way to reduce estimation uncertainty 11 when few data are available at the gauging site. In this work, a model that allows a non 12 null probability to a regional fixed shape parameter is presented. This methodology is inte13 grated within a Bayesian framework and uses reversible jump techniques.(More)