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Our results show that CAV has the best goodness\u2010of\u2010fit statistics of all the GMIMs we have evaluated up to this time. Its relatively small between\u2010 and within\u2010event standard deviations confirm its superior predictability. On the other hand, AI has the highest standard deviation of any GMIM we have studied thus far, which is approximately double that of CAV. Although either CAV or AI or a combination of both have been shown to meet various performance metrics proposed in the context of performance\u2010based earthquake engineering (PBEE), CAV's high level of predictability makes it superior to AI for use in engineering applications, such as PBEE, that involve probabilistic inference.<\/jats:p>","DOI":"10.1193\/090818eqs212m","type":"journal-article","created":{"date-parts":[[2019,6,26]],"date-time":"2019-06-26T15:57:37Z","timestamp":1561564657000},"page":"1289-1310","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":92,"title":["Ground Motion Models for the Horizontal Components of Arias Intensity (AI) and Cumulative Absolute Velocity (CAV) Using the NGA\u2010West2 Database"],"prefix":"10.1002","volume":"35","author":[{"given":"Kenneth W.","family":"Campbell","sequence":"first","affiliation":[{"name":"CoreLogic, Inc. 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