Matrix computations (3rd ed.)
Computational Statistics & Data Analysis
The Handbook of Mathematics and Computational Science
The Handbook of Mathematics and Computational Science
Computer Solution of Large Sparse Positive Definite
Computer Solution of Large Sparse Positive Definite
Bayesian analysis of regression models with spatially correlated errors and missing observations
Computational Statistics & Data Analysis
Handbook of Parallel Computing and Statistics (Statistics, Textbooks and Monographs)
Handbook of Parallel Computing and Statistics (Statistics, Textbooks and Monographs)
Efficient algorithms for estimating the general linear model
Parallel Computing - Parallel matrix algorithms and applications (PMAA'04)
Parallel exact sampling and evaluation of Gaussian Markov random fields
Computational Statistics & Data Analysis
A graph approach to generate all possible regression submodels
Computational Statistics & Data Analysis
Editorial: Spatial statistics: Methods, models & computation
Computational Statistics & Data Analysis
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A parallel method for computing the log of the Jacobian of variable transformations in models of spatial interactions on a lattice is developed. The method is shown to be easy to implement in parallel and distributed computing environments. The advantages of parallel computations are significant even in computer systems with low numbers of processing units, making it computationally efficient in a variety of settings. The non-iterative method is feasible for any sparse spatial weights matrix since the computations involved impose modest memory requirements for storing intermediate results. The method has a linear computational complexity for datasets with a finite Hausdorff dimension. It is shown that most geo-spatial data satisfy this requirement. Asymptotic properties of the method are illustrated using simulated data, and the method is deployed for obtaining maximum likelihood estimates for the spatial autoregressive model using data for the US economy.