Spatial Statistics

Spatial Statistics Software and Spatial Data. The public domain Spatial Statistics Toolbox for Matlab 1.1, 2.0, and Spacestatpack for Fortran 90 excel at estimating large-scale lattice models. The Matlab Spatial Statistics Toolbox includes code for simultaneous spatial autoregressions (SAR), conditional spatial autoregressions (CAR), and mixed regressive spatially autoregressive (MRSA) models. In addition, it contains code for creating sparse spatial weight matrices and finding the log-determinants (needed for maximum likelihood). Hence, the Matlab Spatial Statistics Toolbox includes the most common estimators employed in spatial econometrics. These products use sparse matrices and other computational techniques to greatly accelerate computations and to expand the size of potential data sets analyzed. (Source: http://mathres.kevius.com/software.htm)

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References in zbMATH (referenced in 8 articles )

Showing results 1 to 8 of 8.
Sorted by year (citations)

  1. Wan, Alan T. K.; Xie, Shangyu; Zhou, Yong: A varying coefficient approach to estimating hedonic housing price functions and their quantiles (2017)
  2. Melo, Oscar O.; Mateu, Jorge; Melo, Carlos E.: A generalised linear space-time autoregressive model with space-time autoregressive disturbances (2016)
  3. Ecker, Mark D.; de Oliveira, Victor: Bayesian spatial modeling of housing prices subject to a localized externality (2008)
  4. Lesage, James P.; Charles, Joni S.: Using home buyers’ revealed preferences to define the urban-rural fringe (2008) ioport
  5. Cameron, Trudy Ann: Directional heterogeneity in distance profiles in hedonic property value models (2006)
  6. Kim, Chong Won; Phipps, Tim T.; Anselin, Luc: Measuring the benefits of air quality improvement: A spatial hedonic approach. (2003)
  7. Cerioli, Andrea; Riani, Marco: Robust methods for the analysis of spatially autocorrelated data (2002)
  8. Smirnov, Oleg; Anselin, Luc: Fast maximum likelihood estimation of very large spatial autoregressive models: a characteristic polynomial approach. (2001)