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 data set is also presented.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 45-58 |
| Number of pages | 14 |
| Journal | Computational Statistics and Data Analysis |
| Volume | 122 |
| DOIs | |
| State | Published - Jun 1 2018 |
| Externally published | Yes |
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Keywords
- Estimating equations
- Lag effect
- Low rank approximation
- Statistical efficiency
ASJC Scopus subject areas
- Statistics and Probability
- Computational Mathematics
- Computational Theory and Mathematics
- Applied Mathematics
Cite this
Flexible and efficient estimating equations for variogram estimation. / Sun, Ying; Chang, Xiaohui; Guan, Yongtao.
In: Computational Statistics and Data Analysis, Vol. 122, 01.06.2018, p. 45-58.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Flexible and efficient estimating equations for variogram estimation
AU - Sun, Ying
AU - Chang, Xiaohui
AU - Guan, Yongtao
PY - 2018/6/1
Y1 - 2018/6/1
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 data set 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 data set is also presented.
KW - Estimating equations
KW - Lag effect
KW - Low rank approximation
KW - Statistical efficiency
UR - http://www.scopus.com/inward/record.url?scp=85041438556&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85041438556&partnerID=8YFLogxK
U2 - 10.1016/j.csda.2017.12.006
DO - 10.1016/j.csda.2017.12.006
M3 - Article
VL - 122
SP - 45
EP - 58
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
SN - 0167-9473
ER -