| dc.contributor.author | Griffith, Daniel A. | en |
| dc.date.accessioned | 2015-04-28T11:46:06Z | |
| dc.date.available | 2015-04-28T11:46:06Z | |
| dc.date.issued | 2013-03-08 | en |
| dc.identifier.issn | 1508-2008 | |
| dc.identifier.uri | http://hdl.handle.net/11089/8310 | |
| dc.description.abstract | 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. | en |
| dc.description.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. | en |
| dc.publisher | Wydawnictwo Uniwersytetu Łódzkiego | en |
| dc.relation.ispartofseries | Comparative Economic Research;15 | en |
| dc.rights | This content is open access. | en |
| dc.title | Selected Challenges From Spatial Statistics For Spatial Econometricians | en |
| dc.page.number | 71-85 | en |
| dc.contributor.authorAffiliation | University of Texas at Dallas | en |
| dc.identifier.eissn | 2082-6737 | |
| dc.references | Anselin L. (1988), Spatial Econometrics, Kluwer, Dordrecht | en |
| dc.references | 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 | en |
| dc.references | Besag J. (1974), Spatial interaction and the statistical analysis of lattice systems, ‘J. of the Royal Statistical Society B’, Wiley, New York, 36 | en |
| dc.references | Chaidee N., Tuntapthai M. (2009), Berry-Esséen bounds for random sums of non-i.i.d. randomvariables, ‘International Mathematical Forum’, m-Hikari, Ruse, 4 | en |
| dc.references | Cliff\ A., Ord J. (1969), The Problem of Spatial Autocorrelation, [in:] A. Scott (ed.) LondonPapers in Regional Science, Pion, London | en |
| dc.references | Curry L. (1967), Quantitative geography, 1967, ‘The Canadian Geographer’, Wiley, New York, 11 | en |
| dc.references | Cvetković D., Rowlinson P. (1990), The largest eigenvalue of a graph: a survey, ‘Linear and Multilinear Algebra’, Taylor & Francis, Abingdon, 28 | en |
| dc.references | Gould P. (1967), On the geographical interpretation of eigenvalues, ‘Transactions, Institute of British Geographers’, Wiley, New York, 42 | en |
| dc.references | Griffith D. (1992), Simplifying the normalizing factor in spatial autoregressions for irregularlattices, ‘Papers in Regional Science’, Wiley, New York, 71 | en |
| dc.references | Griffith D. (2004a), Extreme eigenfunctions of adjacency matrices for planar graphs employed inspatial analyses, ‘Linear Algebra & Its Applications’, Elsevier, Amsterdam, 388 | en |
| dc.references | 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 | en |
| dc.references | 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 | en |
| dc.references | Griffith D. (2011b), Positive spatial autocorrelation impacts on attribute variable frequencydistributions, ‘Chilean J. of Statistics’, Sociedad Chilena de Estadística, Valparaiso, 2 (2) | en |
| dc.references | Griffith D., Paelinck J. (2011), Non-standard Spatial Statistics and Spatial Econometrics, Springer-Verlag, Berlin | en |
| dc.references | Maćkiewic, A., Ratajczak W. (1996), Towards a new definition of topological accessibility, ‘Transportation Research B’, Elsevier, Amsterdam, 30 | en |
| dc.references | Mass C. (1985), Computing and interpreting the adjacency spectrum of traffic networks, ‘Journal of Computational and Applied Mathematics’, Elsevier, Amsterdam, 12&13 | en |
| dc.references | Ord J. (1975), Estimation methods for models of spatial interactions, ‘Journal of the American Statistical Association’, Taylor & Francis, Abingdon, 70 | en |
| dc.references | Pace R., LeSage J. (2004), Chebyshev approximation of log-determinants of spatial weightmatrices, ‘Computational Statistics and Data Analysis’, Elsevier, Amsterdam, 45 | en |
| dc.references | 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 | en |
| dc.references | Paelinck J., and Klaassen L. (1979), Spatial Econometrics, Saxon House, Farnborough | en |
| dc.references | 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 | en |
| dc.references | 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 | en |
| dc.references | 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 ThomsonISI: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=000253266000005&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=b7bc2757938ac7a7a821505f8243d9f3 | en |
| dc.references | 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 | en |
| dc.identifier.doi | 10.2478/v10103-012-0027-5 | en |
