Warianty tytułu
Wybrane wyzwania statystyki przestrzennej dla ekonometryków przestrzennych
Języki publikacji
Abstrakty
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. (original abstract)
Artykuł prezentuje wybrane, niestandardowe statystyki przestrzenne oraz zagadnienia ekonometrii przestrzennej. Rozważania teoretyczne koncentrują się na wyzwaniach wynikających z autokorelacji przestrzennej, nawiązując do pojęć Gaussowskiej zmiennej losowej, topologicznych cech danych georeferencyjnych, wektorów własnych, filtrów przestrzennych, georeferencyjnych mechanizmów generowania danych oraz interpretacji efektów losowych. (abstrakt oryginalny)
Twórcy
autor
- University of Texas at Dallas, USA
Bibliografia
- Anselin L. (1988), Spatial Econometrics, Kluwer, Dordrecht.
- Bentkus V., Bloznelis M., Götze F. (1996), A Berry-Esséen bound for Student's statistic in thenon-i.i.d. case, 'J. of Theoretical Probability', Springer, New York, 9.
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- 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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- Gould P. (1967), On the geographical interpretation of eigenvalues, 'Transactions, Institute of British Geographers', Wiley, New York, 42.
- 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.
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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 [Web of Science]
- 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.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171354049