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

[Amitha Puranik]

Dear Prof. Roger,

Thanks for that suggestion. I will go for a simulation approach then to
identify the appropriate method. Thanks again for your help.

Regards,
Amitha Puranik.




On Tue, 6 Oct, 2020, 6:53 PM Roger Bivand, <Roger.Bivand using nhh.no> wrote:

> 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.
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> > 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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> >>>> R-sig-Geo using r-project.org
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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
> >>>
> >>
> >
> >       [[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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