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2012 | 15 | 4 | 71-85

Article title

Selected Challenges From Spatial Statistics For Spatial Econometricians

Content

Title variants

Languages of publication

EN

Abstracts

EN
Griffith and Paelinck (2011) present selected non-standard spatial statistics and spatial econometrics topics that address issues associated with spatial econometric methodology. This paper addresses the following challenges posed by spatial autocorrelation alluded to and/or derived from the spatial statistics topics of this book: the Gaussian random variable Jacobian term for massive datasets; topological features of georeferenced data; eigenvector spatial filtering-based georeferenced data generating mechanisms; and, interpreting random effects.

Keywords

Year

Volume

15

Issue

4

Pages

71-85

Physical description

Dates

published
2012-12-01
online
2013-03-08

Contributors

  • Ph.D., University of Texas at Dallas

References

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  • Besag J. (1974), Spatial interaction and the statistical analysis of lattice systems, ‘J. of the Royal Statistical Society B’, Wiley, New York, 36
  • Chaidee N., Tuntapthai M. (2009), Berry-Esséen bounds for random sums of non-i.i.d. randomvariables, ‘International Mathematical Forum’, m-Hikari, Ruse, 4
  • Cliff\ A., Ord J. (1969), The Problem of Spatial Autocorrelation, [in:] A. Scott (ed.) LondonPapers in Regional Science, Pion, London
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  • Griffith D. (1992), Simplifying the normalizing factor in spatial autoregressions for irregularlattices, ‘Papers in Regional Science’, Wiley, New York, 71
  • Griffith D. (2004a), Extreme eigenfunctions of adjacency matrices for planar graphs employed inspatial analyses, ‘Linear Algebra & Its Applications’, Elsevier, Amsterdam, 388
  • Griffith G. (2004b), Faster maximum likelihood estimation of very large spatial autoregressivemodels: an extension of the Smirnov-Anselin result, ‘J. of Statistical Computation and Simulation’, Taylor & Francis, Abingdon, 74
  • Griffith D. (2011a), Positive spatial autocorrelation, mixture distributions, and geospatial datahistograms, [in:] Y. Leung, B. Lees, C. Chen, C. Zhou, and D. Guo (eds.), ‘Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM 2011)’ , IEEE, Beijing
  • Griffith D. (2011b), Positive spatial autocorrelation impacts on attribute variable frequencydistributions, ‘Chilean J. of Statistics’, Sociedad Chilena de Estadística, Valparaiso, 2 (2)
  • Griffith D., Paelinck J. (2011), Non-standard Spatial Statistics and Spatial Econometrics, Springer-Verlag, Berlin
  • Maćkiewic, A., Ratajczak W. (1996), Towards a new definition of topological accessibility, ‘Transportation Research B’, Elsevier, Amsterdam, 30
  • Mass C. (1985), Computing and interpreting the adjacency spectrum of traffic networks, ‘Journal of Computational and Applied Mathematics’, Elsevier, Amsterdam, 12&13
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  • Pace R., LeSage J. (2004), Chebyshev approximation of log-determinants of spatial weightmatrices, ‘Computational Statistics and Data Analysis’, Elsevier, Amsterdam, 45
  • Paelinck J. (2012), Some challenges for spatial econometricians, paper presented at the 2nd International Scientific Conference about Spatial Econometrics and Regional Economic Analys is, University of Lodz, Poland
  • Paelinck J., and Klaassen L. (1979), Spatial Econometrics, Saxon House, Farnborough
  • Smirnov O., Anselin L. (2001), Fast maximum likelihood estimation of very large spatialautoregressive models: a characteristic polynomial approach, ‘Computational Statistics and Data Analysis’, Elsevier, Amsterdam,, 35
  • Smirnov O., Anselin L. (2009), An O(N) parallel method of computing the log-Jacobian of thevariable transformation for models with spatial interaction on a lattice, ‘Computational Statistics and Data Analysis’, Elsevier, Amsterdam, 53
  • Walde J., Larch M., Tappeiner G. (2008), Performance contest between MLE and GMM for hugespatial autoregressive models, ‘J. of Statistical Computation and Simulation’, Taylor & Francis, Abingdon, 78[WoS]
  • Zhang Y., Leithead W. (2007), Approximate implementation of the logarithm of the matrixdeterminant in Gaussian process regression, ‘J. of Statistical Computation and Simulation’, Taylor & Francis, Abingdon, 77

Document Type

Publication order reference

Identifiers

YADDA identifier

bwmeta1.element.hdl_11089_8310
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