[R-sig-Geo] Spatial data when the missing mechanism is MNAR (non-ignorable)

[Amitha Puranik]

Dear Roger,

Thank you for the quick response. I shall refer to the articles that
you recommended.
Thanks again!

Regards,
Amitha Puranik.

On Mon, Oct 5, 2020 at 1:48 PM Roger Bivand <Roger.Bivand using nhh.no> wrote:

> On Sun, 4 Oct 2020, Amitha Puranik wrote:
>
> > Is it possible to impute missing values in spatial data when the
> > missingness is *MNAR (non-ignorable)*? Can pattern mixture model or
> > selection model be modified to incorporate autocorrelation property and
> > used in this context?
> > Any suggestion/opinion is appreciated.
>
> MNAR possibly means "missing not at random". I see
> https://doi.org/10.1186/1476-072X-14-1 for point support data using INLA.
> For lattice data, see perhaps https://doi.org/10.1007/s10109-019-00316-z
> and work by Thomas Suesse https://doi.org/10.1016/j.csda.2017.11.004
> https://doi.org/10.1080/00949655.2017.1286495. This might be relevant:
> https://doi.org/10.1016/j.epsr.2020.106640, but be extremely careful of
> imputation in training/test settings with spatial data, as the spatial (or
> temporal or both) lead to information leaking between the training and
> test data because they are no longer independent.
>
> Hope this helps,
>
> Roger
>
> >
> > Thanks in advance,
> > Amitha Puranik
> >
> >       [[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 using nhh.no
> https://orcid.org/0000-0003-2392-6140
> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en
>

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