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Re: GEOSTATS: large datasets

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  • Yoshiro Nagao
    I am sorry that I cannot be helpful, but I am interested in maximum likelihood regression analysis by SAS applied to spatial dataset. Could you elaborate it
    Message 1 of 3 , Jun 3, 1998
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      I am sorry that I cannot be helpful, but
      I am interested in maximum likelihood regression analysis
      by SAS applied to spatial dataset.

      Could you elaborate it farther, I mean, are there
      any geostatistical module for SAS?

      "Marcia Gumpertz" <gumpertz@...> sama said:
      >I'm interested in doing a regression with spatially correlated errors. There
      >are several regressor variables. Usually I would use SAS PROC MIXED to
      >estimated all the parameters at once using maximum likelihood, but this dataset
      >has about 4000 observations. I'd be happy if I could just do estimated
      >generalized least squares given a known (assumed) covariance matrix, but I have
      >a strong feeling any program is going to balk at inverting such a large
      >covariance matrix. What do you all do in a situation like this?

      Yoshiro Nagao ( Y-Nagao )
      International Centre for Medical Research
      Kobe University School of Medicine
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    • Robert K. Pace
      With regard to large datasets, I cannot comment on how to estimate spatial autoregressions in SAS. I have estimated a lattice model in S-PLUS (SAR) using 3,107
      Message 2 of 3 , Jun 4, 1998
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        With regard to large datasets, I cannot comment on how to estimate spatial
        autoregressions in SAS. I have estimated a lattice model in S-PLUS (SAR)
        using 3,107 observations. This took around 20 minutes on a 200 Mhz Pentium
        Pro.

        I have published some papers about how to estimate large lattice models - I
        have dealt with as many as 200K+ observations using some of these
        techniques. I currently have two software packages I have written which I
        am distributing for free (with correspondingly little emphasis on GUI).

        I have a Fortran 90 based package with PC executable code. This relies on
        nearest neighbors for specifying the spatial relations. One can choose the
        number of neighbors and the weight given to them. To conduct maximum
        likelihood you need the log of the determinant of the variance-covariance
        matrix or its inverse. This package uses an approximation (which yields
        confidence intervals) for the log-determinant. We have handled matrices as
        large as 1M by 1M using this technique (see Barry and Pace, Linear Algebra
        and its Applications, forthcoming).

        Given the log of the determinant, we estimate via ML the model:
        Y=X*B1+S*X*B2+alpha*S*Y+e, where S is the n by n spatial weight matrix (we
        take the log-determinant of (I-alpha*S)). This corresponds to having
        separately spatially lagged independent and dependent variables. It takes
        under 10 seconds on a Pentium 233 MMX to find the neighbors, estimate the
        log-determinant, and compute the maximum likelihood estimates for the 3,107
        observation dataset. It yields profile likelihoods for the overall model
        and many submodels. There are separate profile likelihoods for the lower
        and upper bounds of the log-determinant and hence the model accounts for
        this source of uncertainty. Hence, one can conduct likelihood ratio tests
        easily.

        Compressed this package with an example and some documentation takes under
        1MB compressed and hence I can email it to whoever wishes to use it.

        I have a second more comprehensive (at least for lattice models) package
        written in Matlab. This does SAR, CAR, the model above, has Delaunay and
        nearest neighbor weight matrices, and simulation routines. This also has
        several example datasets and so forth. This package takes around 30MB and
        cannot be so easily sent. If anyone truly wants it right away, I can burn a
        CDROM and send it. However, I am planning on duplicating this on CDROM and
        sending out some copies at the end-of-the month for those without a
        pressing need.

        Anyone who is interested in receiving these please send a message to
        kpace@... and put "spatial package" as the first two words
        of the subject. I will send you either or both of these toolboxes.

        Kelley Pace
        Real Estate Research Institute
        E.J. Ourso College of Business
        Louisiana State University
        Baton Rouge, LA 70803

        email: kpace@...

        also

        email kelleypace@...

        phone: 504-388-6257
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