College of Business

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.