Flexible and efficient estimating equations for variogram estimation

Ying Sun, Xiaohui Chang, Yongtao Guan

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)45-58
Number of pages14
JournalComputational Statistics & Data Analysis
StatePublished - Jan 11 2018

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KAUST Repository Item: Exported on 2020-10-01


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