摘要：Flash °oods present a recurring problem in many parts of the Southwest United States, and the impact of such °oods is felt on a social as well as an economic scale. The extent and severity of the damage resulting from such °oods has been measured in many ways, including lives lost and dollar amounts of insurance claims. We focus on one historic US river, the Sabine, which has caused extensive °ood damage in the past. Gauge height measurements along segments of the river permit an investigation into the distribution of the height of water at that location in the river over time. The height of water in a river is a function of not only current rainfall and snow melt, but also the geometry of the river itself and numerous characteristics of its surrounding areas, such as permeability of the surrounding soil and extent of human development. Quantifying some of these characteristics for direct incorporation into a model may be challenging in some instances, and the data itself may simply be unavailable in others. Consequently, as an alternative, an expert familiar with river °ow may be able to indirectly impart some of this information to the model through quantiles of the quantity of interest, in this case, gauge height. Proper prior elicitation is a key element in Bayesian inference and the assessment of any prior distribution from experts' opinions is a critical aspect of this inference, both in getting the information and in transforming it into a functional form for the prior distribution. Many methods have been proposed to tackle the problem; most of them are based on the assessment of some features (e.g., quantiles, mean) of the parameter of interest, whereas very few look at features of the model itself, i.e., the observable quantities whose distribution is speciˉed as a function of the parameter. We propose a novel approach which starts from quantiles of the parametric model, translates them into values of the parameters of interest, and uses them to specify a prior distribution. In conjunction with the likelihood, the prior is then used to develop the predictive distribution, which provides the basis for future expectations regarding the behavior of the river. The generalized extreme value distribution will be shown to model the height of water in the Sabine River quite well and we will discuss practical issues concerning the implementation of the approach, from graphical tools helpful in assessing the plausibility of the speciˉed quantiles to adequate parameter transformations and sensitivity analysis.css
关键词：Bayesian robustness ￠ Generalized extreme value distribution ￠ Prior;elicitation ￠ Quantile.
