TY - JOUR
T1 - Flexible and efficient estimating equations for variogram estimation
AU - Sun, Ying
AU - Chang, Xiaohui
AU - Guan, Yongtao
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2018/1/11
Y1 - 2018/1/11
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/626860
UR - http://www.sciencedirect.com/science/article/pii/S016794731830001X
UR - http://www.scopus.com/inward/record.url?scp=85041438556&partnerID=8YFLogxK
U2 - 10.1016/j.csda.2017.12.006
DO - 10.1016/j.csda.2017.12.006
M3 - Article
SN - 0167-9473
VL - 122
SP - 45
EP - 58
JO - Computational Statistics & Data Analysis
JF - Computational Statistics & Data Analysis
ER -