Could it be reasonable to analyze a geostatistical data using spatial autoregreesive models (i.e. SAR and CAR) by treating the geostatistical data as it was observed on a discrete indexing set instead of continuous indexing set?
Can we fit SAR/CAR model for geostatistical data
2 messages · Bedilu Ejigu, Roger Bivand
I don't think this is a productive avenue, as geostatistics is mostly
about prediction, but SAR/CAR are mostly about mitigating
mis-specification problems in inference from covariates. This means that
prediction say from SAR/CAR fitted models is less well understood, for SAR
see for example Michel Goulard, Thibault Laurent & Christine Thomas-Agnan,
2017 \emph{About predictions in spatial autoregressive models: optimal and
almost optimal strategies}, Spatial Economic Analysis Volume 12, Issue
2--3, 304--325 https://doi.org/10.1080/17421772.2017.1300679.
However, INLA (and others) use GMRF in building prediction models, so the
lattice (mesh) may play a role. See for example:
https://becarioprecario.bitbucket.io/spde-gitbook/
http://www.r-inla.org/spde-book
Hope this helps,
Roger
On Mon, 21 Jan 2019, Bedilu Ejigu wrote:
Could it be reasonable to analyze a geostatistical data using spatial autoregreesive models (i.e. SAR and CAR) by treating the geostatistical data as it was observed on a discrete indexing set instead of continuous indexing set? [[alternative HTML version deleted]]
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Roger Bivand Department of Economics, Norwegian School of Economics, Helleveien 30, N-5045 Bergen, Norway. voice: +47 55 95 93 55; e-mail: Roger.Bivand at nhh.no https://orcid.org/0000-0003-2392-6140 https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en