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 2018 |
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
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Cite this
Sun, Y., Chang, X., & Guan, Y. (2018). Flexible and efficient estimating equations for variogram estimation. Computational Statistics and Data Analysis, 122, 45-58. https://doi.org/10.1016/j.csda.2017.12.006