Abstract
When n individuals choose a standard according to “local rules” depending on their position on a set S = 1,2,... , n, there is a spatial correlation occuring between these choices. On the contrary, other situations lead to spatial independency. We study here some models with or without spatial coordination and present tests for absence of spatial coordination.
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References
Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems (with discussion). Journal of the Royal Statistical Society. Series B. Methodological 36, 192–236.
Besag, J.E. and P.A.P. Moran (1975). On the estimation and testing of spatial interaction for Gaussian lattice processes. Biometrika 62, 555–562.
Bolthausen, E. (1982). On the central limit theorem for stationary mixing random fields. The Annals of Probability 10, 1047–1050.
Bouzitat, C. and C. Hardouin (2000). Adoption de standards avec coordination spatiale: une approche empirique. (in preparation).
Cliff, A.D. and J.K. Ord (1981). Spatial autocorrelation (2nd ed.). London: Pion.
Feller, W. (1957). An Introduction to Probability Theory and its Applications (2nd ed.), Volume 1. New York: Wiley.
Geman, D. (1990). Random fields and inverse problem in imaging. In École d’été de Probabilités de Saint-Flour XVIII—1988, Volume 1427 of Lectures Notes in Mathematics, pp. 113–193. Berlin: Springer.
Geman, S. and D. Geman (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 721–741.
Guyon, X. (1995). Random Fields on a Network: Modeling, Statistics and Applications. Probability and its Applications. New York: Springer.
Isaacson, D.L. and R.Q. Madsen (1976). Markov Chain. Theory and Applications. Wiley Series in Probability and Mathematical Statistic. Wiley.
Kemeny, J.G. and J. L. Snell (1960). Finite Markov Chain. The University Series in Undergraduate Mathematics. Van Nostrand.
Pickard, D.K. (1987). Inference for Markov fields: the simplest non-trivial case. Journal of the American Statistical Association 82, 90–96.
Prüm, B. (1986). Processus sur un réseau et mesure de Gibbs. Applications. Techniques Stochastiques. Paris: Masson.
Robert, C. (1996). Méthodes de simulation en statistiques. Economica.
Trouvé, A. (1988). Problèmes de convergence et d’ergodicité pour les algo-rithmes de recuit parallélisés. Comptes Rendus des Séances de l’Académic des Sciences. Série I. Mathématique 307, 161–164.
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Hardouin, C., Guyon, X. (2001). Standards Adoption Dynamics and Test for Nonspatial Coordination. In: Moore, M. (eds) Spatial Statistics: Methodological Aspects and Applications. Lecture Notes in Statistics, vol 159. Springer, New York, NY. https://doi.org/10.1007/978-1-4613-0147-9_3
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DOI: https://doi.org/10.1007/978-1-4613-0147-9_3
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-95240-6
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