<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Diks, C. G. H.</style></author><author><style face="normal" font="default" size="100%">J.A. Vrugt</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparison of point forecast accuracy of model averaging methods in hydrologic applications</style></title><secondary-title><style face="normal" font="default" size="100%">Stochastic Environmental Research and Risk Assessment</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Stoch. Environ. Res. Risk Assess.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">1172</style></keyword><keyword><style  face="normal" font="default" size="100%">Bates-Granger weights</style></keyword><keyword><style  face="normal" font="default" size="100%">Bayesian model averaging</style></keyword><keyword><style  face="normal" font="default" size="100%">Content Type: Biblio</style></keyword><keyword><style  face="normal" font="default" size="100%">Granger-Ramanathan</style></keyword><keyword><style  face="normal" font="default" size="100%">Mallows model averaging</style></keyword><keyword><style  face="normal" font="default" size="100%">optimization</style></keyword><keyword><style  face="normal" font="default" size="100%">pressure head</style></keyword><keyword><style  face="normal" font="default" size="100%">runoff</style></keyword><keyword><style  face="normal" font="default" size="100%">simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Streamflow forecasting</style></keyword><keyword><style  face="normal" font="default" size="100%">Tensiometric</style></keyword><keyword><style  face="normal" font="default" size="100%">uncertainty</style></keyword><keyword><style  face="normal" font="default" size="100%">weights</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Aug</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">&lt;Go to ISI&gt;://000279605300002</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">6</style></number><volume><style face="normal" font="default" size="100%">24</style></volume><pages><style face="normal" font="default" size="100%">809-820</style></pages><isbn><style face="normal" font="default" size="100%">1436-3240</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Multi-model averaging is currently receiving a surge of attention in the atmospheric, hydrologic, and statistical literature to explicitly handle conceptual model uncertainty in the analysis of environmental systems and derive predictive distributions of model output. Such density forecasts are necessary to help analyze which parts of the model are well resolved, and which parts are subject to considerable uncertainty. Yet, accurate point predictors are still desired in many practical applications. In this paper, we compare a suite of different model averaging techniques by their ability to improve forecast accuracy of environmental systems. We compare equal weights averaging (EWA), Bates-Granger model averaging (BGA), averaging using Akaike's information criterion (AICA), and Bayes' Information Criterion (BICA), Bayesian model averaging (BMA), Mallows model averaging (MMA), and Granger-Ramanathan averaging (GRA) for two different hydrologic systems involving water flow through a 1950 km(2) watershed and 5 m deep vadose zone. Averaging methods with weights restricted to the multi-dimensional simplex (positive weights summing up to one) are shown to have considerably larger forecast errors than approaches with unconstrained weights. Whereas various sophisticated model averaging approaches have recently emerged in the literature, our results convincingly demonstrate the advantages of GRA for hydrologic applications. This method achieves similar performance as MMA and BMA, but is much simpler to implement and use, and computationally much less demanding.&lt;/p&gt;</style></abstract><work-type><style face="normal" font="default" size="100%">Article</style></work-type><accession-num><style face="normal" font="default" size="100%">ISI:000279605300002</style></accession-num><notes><style face="normal" font="default" size="100%">&lt;p&gt;Diks, Cees G. H. Vrugt, Jasper A.Sp. Iss. SI&lt;/p&gt;</style></notes></record></records></xml>