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A Bayesian Analysis of Extreme Rainfall Data

Stuart G. Coles and Jonathan A. Tawn
Journal of the Royal Statistical Society. Series C (Applied Statistics)
Vol. 45, No. 4 (1996), pp. 463-478
Published by: Wiley for the Royal Statistical Society
DOI: 10.2307/2986068
Stable URL: http://www.jstor.org/stable/2986068
Page Count: 16
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A Bayesian Analysis of Extreme Rainfall Data
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Abstract

Understanding and quantifying the behaviour of a rainfall process at extreme levels has important applications for design in civil engineering. As in the extremal analysis of any environmental process, estimates often are required of the probability of events that are rarer than those already recorded. As data on extremes are scarce, all available sources of information should be used in inference. Consequently, research has focused on the development of techniques that make optimal use of available data. In this paper a daily rainfall series is analysed within a Bayesian framework, illustrating how the careful elicitation of prior expert information can supplement data and lead to improved estimates of extremal behaviour. For example, using the prior knowledge of an expert hydrologist, a Bayesian 95% interval estimate of the 100-year return level for daily rainfall is found to be approximately half of the width of the corresponding likelihood-based confidence interval.

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