http://www.cnr.it/ontology/cnr/individuo/prodotto/ID64933
Bayesian modeling of flash floods using generalized extreme value distribution with prior elicitation (Articolo in rivista)
- Type
- Label
- Bayesian modeling of flash floods using generalized extreme value distribution with prior elicitation (Articolo in rivista) (literal)
- Anno
- 2010-01-01T00:00:00+01:00 (literal)
- Alternative label
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Dey D., Gajoni E., Ruggeri F. (literal)
- Pagina inizio
- Pagina fine
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
- http://chjs.soche.cl/papers/volumen1_2010/ChJS-01-01-05.pdf (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
- Rivista
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Watson Research Center, IBM, New York, USA;
University of Connecticut, Statistics Department, Connecticut, USA;
CNR IMATI, Milano, Italy (literal)
- Titolo
- Bayesian modeling of flash floods using generalized extreme value distribution with prior elicitation (literal)
- Abstract
- 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 speci¯ed 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 speci¯ed quantiles to adequate parameter transformations and
sensitivity analysis. (literal)
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