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Computer model calibration with confidence and consistency
Author(s) -
Plumlee Matthew
Publication year - 2019
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/rssb.12314
Subject(s) - confidence region , calibration , computation , consistency (knowledge bases) , confidence interval , set (abstract data type) , computer science , sample (material) , confidence distribution , sample space , sample size determination , statistics , low confidence , algorithm , coverage probability , mathematics , artificial intelligence , psychology , social psychology , chemistry , chromatography , programming language
Summary The paper proposes and examines a calibration method for inexact models. The method produces a confidence set on the parameters that includes the best parameter with a desired probability under any sample size. Additionally, this confidence set is shown to be consistent in that it excludes suboptimal parameters in large sample environments. The method works and the results hold with few assumptions; the ideas are maintained even with discrete input spaces or parameter spaces. Computation of the confidence sets and approximate confidence sets is discussed. The performance is illustrated in a simulation example as well as two real data examples.