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Combining site‐specific and regional information: An empirical Bayes Approach
Author(s) -
Kuczera George
Publication year - 1982
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/wr018i002p00306
Subject(s) - bayes' theorem , estimator , structural basin , computer science , inference , context (archaeology) , econometrics , statistics , data mining , bayesian probability , mathematics , geography , artificial intelligence , geology , paleontology , archaeology
Empirical Bayes theory, adapted to a hydrologic context, is used to develop procedures for inferring hydrologic quantities by combining site‐specific and regional information. It ‘borrows strength’ from ‘similar’ basins to improve upon inference at a particular basin. The superpopulation is a key concept in the empirical Bayes approach. It is a probability distribution from which basin parameters are randomly assigned, a conceptualization closely related to regionalization models. It is inferred from observable regional data and expresses the degree of basin ‘similarity’ in a region. This approach treats regionalized estimators as a special case and leads to procedures similar to James‐Stein estimators. Empirical Bayes procedures can lead to substantial improvements in performance over site‐specific procedures. However, for basins which are very different from the majority, site‐specific procedures may perform better. The method of moments approach to inferring the superpopulation is considered in detail. Finally, two examples in flood frequency analysis are presented to illustrate various facets of empirical Bayes procedures.