Efficient likelihood computations for some multivariate Gaussian Markov random fields

L. Ippoliti, R.J. Martin and L. Romagnoli

Journal of Multivariate Analysis, 2018, vol. 168, issue C, 185-200

Abstract: Data collected from spatial locations are often multivariate. Gaussian conditional autoregressive (CAR) models, also known as Gaussian Markov random fields, are frequently used to analyze such continuous data, or as models for the parameters of discrete distributions. Two difficulties in Gaussian maximum likelihood estimation are ensuring that the parameter estimates are allowable values, and computing the likelihood efficiently. It is shown here that, for some commonly-used multivariate CAR models, checking for allowable parameter values can be facilitated, and the likelihood can be computed very quickly.

Keywords: Conditional autoregressive model; Gaussian Markov random fields; Lattice data; Maximum likelihood estimation; Multivariate observations; Regional data (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.jmva.2018.07.007

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