Flexible and efficient estimating equations for variogram estimation

Handle URI:
http://hdl.handle.net/10754/626860
Title:
Flexible and efficient estimating equations for variogram estimation
Authors:
Sun, Ying ( 0000-0001-6703-4270 ) ; Chang, Xiaohui ( 0000-0001-7779-6324 ) ; Guan, Yongtao ( 0000-0002-9683-9059 )
Abstract:
Variogram 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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Applied Mathematics and Computational Science Program
Citation:
Sun 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.
Publisher:
Elsevier BV
Journal:
Computational Statistics & Data Analysis
Issue Date:
11-Jan-2018
DOI:
10.1016/j.csda.2017.12.006
Type:
Article
ISSN:
0167-9473
Additional Links:
http://www.sciencedirect.com/science/article/pii/S016794731830001X
Appears in Collections:
Articles; Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorSun, Yingen
dc.contributor.authorChang, Xiaohuien
dc.contributor.authorGuan, Yongtaoen
dc.date.accessioned2018-01-28T07:01:36Z-
dc.date.available2018-01-28T07:01:36Z-
dc.date.issued2018-01-11en
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.en
dc.identifier.issn0167-9473en
dc.identifier.doi10.1016/j.csda.2017.12.006en
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.en
dc.publisherElsevier BVen
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S016794731830001Xen
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/en
dc.subjectEstimating equationsen
dc.subjectLag effecten
dc.subjectLow rank approximationen
dc.subjectStatistical efficiencyen
dc.titleFlexible and efficient estimating equations for variogram estimationen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.identifier.journalComputational Statistics & Data Analysisen
dc.eprint.versionPost-printen
dc.contributor.institutionCollege of Business, Oregon State University, USAen
dc.contributor.institutionDepartment of Management Science, University of Miami, USAen
kaust.authorSun, Yingen
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