Pseudo Best Estimator by a Separable Approximation of Spatial Covariance Structures

抄録

We consider a linear regression model with a spatially correlated error term on a lattice. When estimating coefficients in the linear regression model, the generalized least squares estimator (GLSE) is used if the covariance structures are known. However, the GLSE for large spatial data sets is computationally expensive because of the matrix inversion. To reduce the computational complexity, we propose a pseudo best estimator (PBE) using spatial covariance structures approximated by separable covariance functions and derive its asymptotic covariance matrix. Monte Carlo simulations demonstrate that our proposed PBE performs well.