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

[Roger Bivand]

On Tue, 6 Oct 2020, Amitha Puranik wrote:

> Dear Prof Roger,
>
> This is in continuation to my previous query on spatial data imputation
> with MNAR mechanism. I have gone through the references recommended by you
> and have the following concerns which I request you to address.
>
> 1. The papers by Thomas Suesse, Takafumi Kato suggest likelihood based
> approaches for predicting missing data in simultaneous autoregressive
> models with an assumption of *missing at random* mechanism.
>
> 2. The additional reference provided by you, i.e. 'Missing Data in Wind
> Farm Time Series: Properties and Effect on Forecasts' by Tawn et al.,
> assume *missing not at random* mechanism in an autoregressive framework and
> have applied mean imputation and multiple imputation methods.
>
>
>
> I am presently looking for a technique to deal with MNAR in spatially
> autocorrelated data. Would it be reasonable to apply the methods
> recommended by Suesse or Kato in this scenario by ignoring the missing
> mechanism?
>
> From what I understand, using conventional methods that are effective for
> MAR case would produce biased estimates when data is MNAR. Can the approach
> applied by Tawn et al. (i.e. mean imputation or multiple imputation) be
> used on spatial data with MNAR mechanism?
>

Since probably nobody knows, you may very well need to run simulations in 
the settings you need to determine which outcomes correspond to reasonable 
practice. Reviewers of you work would probably appreciate your having 
explored the robustness of your choice of method. Quite a lot will depend 
on the spatial support of your data too.

Roger

>
>
> Any comment/ suggestion will be appreciated.
>
>
> Thanks in advance,
>
> Amitha Puranik.
>
>
>
>
>
>
>
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>
>
>
> On Mon, Oct 5, 2020 at 2:13 PM Amitha Puranik <puranik.amitha using gmail.com>
> wrote:
>
>> 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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-- 
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