Global optimization of statistical functions with simulated annealing.
Many statistical methods rely on numerical optimization to estimate a model's parameters. Unfortunately, conventional algorithms sometimes fail. Even when they do converge, there is no assurance that they have found the global, rather than a local, optimum. We test a new optimization algorithm, s...
| Published in: | Journal of Econometrics Vol. 60; pp. 65 - 100 |
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| Main Authors: | , , |
| Format: | Article |
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Elsevier Science
January/February 1994
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=512362674&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 512362674 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 03044076 ECM jtl: Journal of Econometrics issn: 03044076 maglogo: N pubinfo: dt: January/February 1994 vid: 60 pid: 1004 pub: Elsevier Science artinfo: ui: 512362674 10.1016/0304-4076(94)90038-8 ppf: 65 ppct: 35 formats: tig: atl: Global optimization of statistical functions with simulated annealing. aug: au: Goffe, William L. Ferrier, Gary D. Rogers, John su: Algorithms Estimation theory Mathematical optimization sug: subj: Algorithms Estimation theory Mathematical optimization ab: Many statistical methods rely on numerical optimization to estimate a model's parameters. Unfortunately, conventional algorithms sometimes fail. Even when they do converge, there is no assurance that they have found the global, rather than a local, optimum. We test a new optimization algorithm, simulated annealing, on four econometric problems and compare it to three common conventional algorithms. Not only can simulated annealing find the global optimum, it is also less likely to fail on difficult functions because it is a very robust algorithm. The promise of simulated annealing is demonstrated on the four econometric problems. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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