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dc.contributor.authorSun, Ying
dc.contributor.authorChang, Xiaohui
dc.contributor.authorGuan, Yongtao
dc.date.accessioned2018-01-28T07:01:36Z
dc.date.available2018-01-28T07:01:36Z
dc.date.issued2018-01-11
dc.identifier.citationSun Y, Chang X, Guan Y (2018) Flexible and efficient estimating equations for variogram estimation. Computational Statistics & Data Analysis. Available: http://dx.doi.org/10.1016/j.csda.2017.12.006.
dc.identifier.issn0167-9473
dc.identifier.doi10.1016/j.csda.2017.12.006
dc.identifier.urihttp://hdl.handle.net/10754/626860
dc.description.abstractVariogram estimation plays a vastly important role in spatial modeling. Different methods for variogram estimation can be largely classified into least squares methods and likelihood based methods. A general framework to estimate the variogram through a set of estimating equations is proposed. This approach serves as an alternative approach to likelihood based methods and includes commonly used least squares approaches as its special cases. The proposed method is highly efficient as a low dimensional representation of the weight matrix is employed. The statistical efficiency of various estimators is explored and the lag effect is examined. An application to a hydrology dataset is also presented.
dc.publisherElsevier BV
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S016794731830001X
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Computational Statistics & Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics & Data Analysis, [, , (2018-01-11)] DOI: 10.1016/j.csda.2017.12.006 . © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectEstimating equations
dc.subjectLag effect
dc.subjectLow rank approximation
dc.subjectStatistical efficiency
dc.titleFlexible and efficient estimating equations for variogram estimation
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalComputational Statistics & Data Analysis
dc.eprint.versionPost-print
dc.contributor.institutionCollege of Business, Oregon State University, USA
dc.contributor.institutionDepartment of Management Science, University of Miami, USA
kaust.personSun, Ying
refterms.dateFOA2020-01-11T00:00:00Z
dc.date.published-online2018-01-11
dc.date.published-print2018-06


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