Penalized pseudolikelihood inference in spatial interaction models with covariates

Scandinavian Journal of Statistics

Volumn 27, Issue 3, 2000, Pages 445-458

  Divino, Fabio ^a   Frigessi, Arnoldo ^b   Green, Peter J ^c  

^a UNIVERSITY OF FLORENCE   (Italy)

^b NORWEGIAN COMPUTING CENTER   (Norway)

^c UNIVERSITY OF BRISTOL   (United Kingdom)

Author keywords

B splines; Bayesian image restoration; Informative priors; Markov random fields; Pseudolikelihood; Smoothing splines

Indexed keywords

EID: 0034258382     PISSN: 03036898     EISSN: None     Source Type: Journal    
DOI: 10.1111/1467-9469.00200     Document Type: Article

References (20)

1. Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems (with discussion). J. Roy. Statist. Soc. Ser.B 36, 192-236.
2. Besag, J., York, J. & Mollié, A. (1991). Bayesian image restoration, with two applications in spatial statistics (with discussion). Ann. Inst. Statist. Math. 43, 1-59.
3. Besag, J., Green, P., Higdon, D. & Mengersen, K. (1995). Bayesian computation and stochastic systems. Statist. Sci. 10, 3-66.
4. Comets, F. (1992). On consistency of a class of estimators for exponential families of Markov random fields on the lattice. Ann. Statist. 19, 455-486.
5. Comets, F. & Janzura, M. (1998). A central limit theorem for conditionally centered random fields with application to Markov fields. Adv. Appl. Probab. 35, 608-621.
6. De Boor, C. (1978). A practical guide to splines. Springer-Verlag, New York.
7. Eubank, R. L. (1988). Spline smoothing and nonparametric regression, Marcel Dekker, New York.
8. Ferrandiz J., López, A., Llopis, A., Morales, M. & Tejerizo, M. L. (1995). Spatial interaction between neighbouring counties: cancer mortality data in Valencia (Spain). Biometrics 51, 665-678.
9. Frigessi, A. & Piccioni, M. (1990). Parameter estimation for the two-dimensional Ising field corrupted by noise. Stochastic Process. Appl. 34, 297-311.
10. Frigessi, A. & Stander, J. (1994). Informative Markov random field priors for the Bayesian classification of satellite images. J. Amer. Statist. Assoc. 89, 703-709.
11. Georgii, H. O. (1988). Gibbs measure and phase transition. De Gruyter, Amsterdam.
12. Green, P. J. & Silverman, B. W. (1994). Nonparametric regression and generalized linear models. Chapman & Hall, London.
13. Guyon, X. (1995). Random fields on a network. Springer-Verlag, Berlin.
14. Hastie, T. J. & Tibshirani, R. J. (1993). Varying-coefficient models (with discussion). J. Roy. Statist. Soc. Ser. B 55, 757-796.
15. Hjort, N. L. & Mohn, E. (1987). Topics in the statistical analysis of remotely sensed data. Bull. Internat. Statist. Inst. 52, 23-44.
16. Jensen, J. L. & Künsch, H. R. (1994). On asymptotic normality of pseudolikelihood estimates for pairwise interaction processes. Ann. Inst. Statist. Math. 46, 475-486.
17. Jensen, J. L. & Møller, J. (1991). Pseudolikelihood for exponential family models of spatial point processes. Ann. Appl. Probab. 1, 445-461.
18. Klein, R. & Press, S. J. (1992). Adaptive Bayesian classification of spatial data. J. Amer. Statist. Assoc. 87, 844-851.
19. Winkler, G. (1995). Image analysis, random fields and dynamic Monte Carlo methods: a mathematical introduction. Springer-Verlag, Berlin.
20. Younes, L. (1991). Maximum likelihood estimation for Gibbs fields. In Spatial statistics and imaging: Proceedings of an AMS-IMS-SIAM Joint Summer Research Conference (ed. A. Possolo), Lecture Notes -Monograph Series, Institute of Mathematical Statistics, Haywood, CA.