[prev in list] [next in list] [prev in thread] [next in thread] 

List:       r-sig-geo
Subject:    Re: [R-sig-Geo] Can we fit SAR/CAR model for geostatistical data
From:       Roger Bivand <Roger.Bivand () nhh ! no>
Date:       2019-01-22 9:49:28
Message-ID: alpine.LFD.2.21.1901221044460.26520 () reclus ! nhh ! no
[Download RAW message or body]

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]]
>
> _______________________________________________
> R-sig-Geo mailing list
> R-sig-Geo@r-project.org
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>

-- 
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@nhh.no
https://orcid.org/0000-0003-2392-6140
https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en

_______________________________________________
R-sig-Geo mailing list
R-sig-Geo@r-project.org
https://stat.ethz.ch/mailman/listinfo/r-sig-geo
[prev in list] [next in list] [prev in thread] [next in thread]