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

[Roger Bivand]

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
>
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>
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-- 
Roger Bivand
Department of Economics, Norwegian School of Economics,
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