Markov random-field models for estimating local labour markets

Journal of the Royal Statistical Society. Series C: Applied Statistics

Volumn 52, Issue 2, 2003, Pages 201-211

  Sebastiani, Maria Rita ^a  

^a SAPIENZA UNIVERSITY OF ROME   (Italy)

Author keywords

Bayesian texture segmentation; Local labour markets; Markov random field models; Partitioning; Simulated annealing algorithm; Spatial modelling

Indexed keywords

EID: 0141461767     PISSN: 00359254     EISSN: None     Source Type: Journal    
DOI: 10.1111/1467-9876.00398     Document Type: Article

References (23)

1. Ball, R. M. (1980) The use and the definition of Travel to Work Areas in Great Britain: some problems. Regl Stud., 14, 125-139.
2. Baxter, M. (1986) Geographical and planning models for data on spatial flows. Statistician, 35, 191-198.
3. Bennett, R. J. and Haining, R. P. (1985) Spatial structure and spatial interaction: modelling approaches to the statistical analysis of geographical data. J. R. Statist. Soc. A, 148, 1-27.
4. Berry, B. J. L. and Horton, F. (1970) Geographical Perspectives in Urban Systems. New York: Prentice Hall.
5. Besag, J. (1975) Statistical analysis of non-lattice data. Statistician, 24, 179-195.
6. Carmichael, C. L. (1978) Local labour market analysis: its importance and a possible approach. Geoforum, 9, 127-148.
7. Coombes, M. G., Green, A. E. and Openshaw, S. (1986) An efficient algorithm to generate official statistical reporting areas: the case of 1984 Travel to Work Areas revision in Britain. J. Ops Res. Soc., 10, 943-953.
8. Dubois, D. and Prade, H. (1980) Fuzzy Sets and Systems: Theory and Applications. New York: Academic Press.
9. Erba, A., D'Angiò, A. and Marzulli, S. (1989) Partizioni funzionali del territorio e sistemi locali del lavoro: il modello Isers. Rass. Statist. Lav., 3, 9-52.
10. Geman, D., Geman, S., Graffigne, C. and Dong, P. (1990) Boundary detection by constrained optimization. IEEE Trans. Pattn Anal. Mach. Intell., 12, 609-628.
11. Geman, S. and Geman, D. (1984) Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans. Pattn Anal. Mach. Intell., 6, 721-741.
12. Geman, S. and Graffigne, C. (1987) Markov random field image models and their applications to computer vision. In Proc. Int. Congr. Mathematicians (ed. A. M. Gleason), pp. 1496-1517. Providence: American Mathematical Society.
13. Grenander, U. (1989) Advances in pattern theory. Ann. Statist., 17, 1-30.
14. Guyon, X. (1987) Estimation d'un champ par pseudo-vraisemblance conditionelle: étude asymptotique et application au cas Markovien. In Spatial Processes and Spatial Time Series Analysis (ed. F. Droesbeke), pp. 15-62. Brussels: Publications des Facultés Universitaires Saint-Louis.
15. Istat-Irpet (1989) I Mercati Locali del Lavoro in Italia. Rome: Angeli.
16. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H. and Teller, E. (1953) Equations of state calculations by fast computing machines. J. Chem. Phys., 21, 1087-1092.
17. Ripley, B. D. (1988) Statistical Inference for Spatial Processes. Cambridge: Cambridge University Press.
18. Sen, A. and Smith, T. E. (1995) Gravity Models of Spatial Interaction Behaviour. New York: Springer.
19. Smart, M. W. (1974) The use and definition of Travel to Work Areas. Progr. Planng, 2, 239-253.
20. Stander, J. and Silverman, B. W. (1994) Temperature schedules for simulated annealing. Statist. Comput., 4, 21-32.
21. Upton, G. J. G. and Fingleton, B. (1989) Spatial Data Analysis by Example. Chichester: Wiley.
22. Winkler, G. (1995) Image Analysis, Random Fields and Dynamic Monte Carlo Methods. Berlin: Springer.
23. Zimmermann, H. J. (1991) Fuzzy Set Theory and Its Application. Dordrecht: Kluwer.