JOURNAL OF THE JAPAN STATISTICAL SOCIETY
Online ISSN : 1348-6365
Print ISSN : 1882-2754
ISSN-L : 1348-6365
Articles
Pseudo Best Estimator by a Separable Approximation of Spatial Covariance Structures
Toshihiro Hirano
Author information
JOURNAL FREE ACCESS

2014 Volume 44 Issue 1 Pages 43-71

Details
Abstract

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.

Content from these authors
© 2014 Japan Statistical Society
Previous article Next article
feedback
Top