Abstract
This book has presented special topics in spatial statistics concerning spatial autoregression that go beyond indexing the nature and degree of spatial autocorrelation latent in a data set, simple trend surface models, description of the spectral counterparts to selected spatial autoregressive models, inclusion of spatially lagged variables, or generalized least squares solutions for linear spatial models. Attention has been directed to the properties of sufficiency, consistency, efficiency and biasedness for statistics calculated on spatial data sets. The issues of configurational information, edge effects, missing data, multivariate linear statistical models, and simulation experimentation have been explored. In other words, selected themes of an advanced nature have been treated here.
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References
Lele, S., and J. Ord, 1986a, Conditional least squares estimation for spatial processes: some asymptotic results, Technical Reports and Reprints No. 65, Department of Statistics, The Pennsylvania State University, University Park.
Lele, S., and J. Ord, 1986b, Besag’s pseudo-likelihood: some optimality results, Technical Reports and Reprints No. 66, Department of Statistics, The Pennsylvania State University, University Park.
Mardia, K., and R. Marshall, 1984, Maximum likelihood estimation of models for residual covariance in spatial regression, Biometrika, Vol. 71: 135–146.
Unwin, D., 1975, An Introduction to Trend Surface Analysis, Norwich, England: Geo Abstracts.
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© 1988 Kluwer Academic Publishers, Dordrecht
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Griffith, D.A. (1988). Summary and Conclusions. In: Advanced Spatial Statistics. Advanced Studies in Theoretical and Applied Econometrics, vol 12. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-2758-2_10
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DOI: https://doi.org/10.1007/978-94-009-2758-2_10
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-7739-2
Online ISBN: 978-94-009-2758-2
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