Residual-based evaluation for spatially-varying processes

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dc.authorscopusid57194819977
dc.contributor.authorTutmez B.
dc.date.accessioned2024-08-04T20:04:07Z
dc.date.available2024-08-04T20:04:07Z
dc.date.issued2017
dc.departmentİnönü Üniversitesien_US
dc.description25th International Mining Congress of Turkey: New Trends in Mining, IMCET 2017 -- 11 April 2017 through 14 April 2017 -- 127813en_US
dc.description.abstractEvaluation of variability in a mining site is one of the main motivations of geosciences uncertainty analysis. In parameter estimation, both maximum-likelihood-based methods and ordinary least squares (OLS) correspond constant variance, which is the variability of a measurement is the same in any case of the values of the explanatory variables connected with it. When the assumption of constant variance is not satisfied, a data transformation and use of generalized least squares regression (GLS) is required. GLS approach considers the inequality of variance in the measurements. In the present study, GLS fitting is performed by residual maximum likelihood (REML) method in which each observation is represented additively in terms of fixed and random effects. By using the Bauxite Mine data, the performances of the REML-based GLS model are also compared with conventional Geographically Weighted Regression (GWR) Model performances and the results are discussed.en_US
dc.identifier.endpage241en_US
dc.identifier.isbn9786050110081
dc.identifier.scopus2-s2.0-85021800665en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage236en_US
dc.identifier.urihttps://hdl.handle.net/11616/92388
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTMMOB Maden Muhendisleri Odasien_US
dc.relation.ispartofIMCET 2017: New Trends in Mining - Proceedings of 25th International Mining Congress of Turkeyen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLeast squares approximationsen_US
dc.subjectMaximum likelihooden_US
dc.subjectMaximum likelihood estimationen_US
dc.subjectMetadataen_US
dc.subjectData transformationen_US
dc.subjectExplanatory variablesen_US
dc.subjectGeneralized least squares regressionsen_US
dc.subjectGeographically weighted regression modelsen_US
dc.subjectMining sitesen_US
dc.subjectOrdinary least squaresen_US
dc.subjectRandom effectsen_US
dc.subjectResidual maximum likelihooden_US
dc.subjectUncertainty analysisen_US
dc.titleResidual-based evaluation for spatially-varying processesen_US
dc.typeConference Objecten_US

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