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Parisi, Antonio (2008) Sampling from a variable dimension mixture model posterior. [Tesi di dottorato]

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Abstract (inglese)

Goal of the thesis is the analysis of a real dataset concerning a biological problem that obtained an increasing interest in recent years. Commercial stocks of fish are not sufficient anymore to satisfy the global demand. Hence, fishermen are beginning to catch species living in the deep. As little is known
about these species, there is an actual risk of extinction of these species.
As it is typically difficult and expensive to gather the ages of fish, in order to implement stock management policies, it is necessary to build up reliable growth models to infer ages from length data. The lengths, if we don't observe the ages, come from a mixture distribution, in which the components are the different cohorts.
As MCMC methods are not always satisfactory for the analysis of mixture models, to estimate the parameters of the model and the number of cohorts that form the sample, it is employed a Population Monte Carlo algorithm for mixtures generalized to the case of unknown number of components.

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Tipo di EPrint:Tesi di dottorato
Relatore:Coles, Stuart
Correlatore:Liseo, Brunero - Robert, Christian
Dottorato (corsi e scuole):Ciclo 20 > Scuole per il 20simo ciclo > SCIENZE STATISTICHE
Data di deposito della tesi:2008
Anno di Pubblicazione:2008
Parole chiave (italiano / inglese):Fishery, mixture models, Population Monte Carlo, variable dimension models
Settori scientifico-disciplinari MIUR:Area 13 - Scienze economiche e statistiche > SECS-S/01 Statistica
Struttura di riferimento:Dipartimenti > Dipartimento di Scienze Statistiche
Codice ID:878
Depositato il:25 Nov 2008
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1. Baldi, P., Russo, T., Parisi, A., Magnifico, G., Mariani, S. & Cataudella, S. (to be submitted). A new stochastic von Bertalanffy model of fish growth, with application to population analysis. Cerca con Google

2. Bhattacharya, C. G. (1967). A simple method for resolution of a distribution into its gaussian components. Biometrics 23, 115-135. Cerca con Google

3. Cappé, O., Guillin, A., Marin, J. M. & Robert, C. P. (2004). Population Monte Carlo. J. Comput. Graph. Statist. 13, 907-929. Cerca con Google

4. Cappé, O., Robert, C. P. & Rydén, T. (2003). Reversible jump, birth-and-death and more general continuous time markov chain monte carlo samplers. Journal Of The Royal Statistical Society Series B 65, 679-700. Available at Vai! Cerca con Google

5. Celeux, G. & Diebolt, J. (1985). The sem algorithm: a probabilistic teacher algorithm derived from the em algorithm for the mixture problem. Comput. Statist. Quater. 2, 73-82. Cerca con Google

6. Celeux, G., Hurn, M. & Robert, C. P. (2000). Computational and inferential difficulties with mixture posterior distributions. J. Amer. Statist. Assoc. 95, 957-970. Cerca con Google

7. Celeux, G., Marin, J.-M. & Robert, C. P. (2006). Iterated importance sampling in missing data problems. Comput. Statist. Data Anal. 50, 3386-3404. Cerca con Google

8. Chib, S. (1995). Marginal likelihood from the Gibbs output. J. Amer. Statist. Assoc. 90, 1313-1321. Cerca con Google

9. Dempster, A. P., Laird, N. M. & Rubin, D. B. (1977). Maximum likelihood from incomplete data via de EM algorithm. The Journal of Royal Statistical Society 39, 1-37. Cerca con Google

10. Diebolt, J. & Robert, C. (1994). Estimation of finite mixture distributions through Bayesian sampling. Journal of the Royal Statistical Society series B 56, 363-375. Cerca con Google

11. Douc, R., Guillin, A., Marin, J. M. & Robert, C. P. (2007). Convergence of adaptive mixtures of importance sampling schemes 35. Comment: Published at in the Annals of Statistics ( by the Institute of MathematicalStatistics ( Vai! Cerca con Google

12. Escobar, M. D. & West, M. (1995). Bayesian density estimation and inference using mixtures. Journal of the American Statistical Association 90, 577-588. Cerca con Google

13. Feller, W. (1943). On a general class of “contagious” distributions. The Annals of Mathematical Statistics 14, 389-400. Cerca con Google

14. Fruhwirth-Schnatter, S. (2001). Markov chain monte carlo estimation of classical and dynamic switching and mixture models. Journal of the American Statistical Association 96, 194-209 (16). Cerca con Google

15. Gelfand, A. E. & Smith, A. F. M. (1990). Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association 85, 398-409. Cerca con Google

16. Geweke, J. F. (1989). Bayesian inference of econometric models using monte carlo integration. Econometrica 57, 1317-1339. Cerca con Google

17. Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82, 711-732. Cerca con Google

18. Guihenneuc-Jouyaux, C., Mengersen, K. & Robert, C. (1998). Mcmc convergence diagnostics: A "reviewww". Papers 9816, Institut National de la Statistique et des Etudes Economiques. Available at Vai! Cerca con Google

19. Jasra, A., Holmes, C. C. & Stephens, D. A. (2005). Markov chain monte carlo methods and the label switching problem in bayesian mixture modeling. Statistical Science 20, 50-67. Cerca con Google

20. Kiefer, J. & Wolfowitz, J. (1956). Consistency of the maximum likelihood estimator in the presence of infinitely many nuisance parameters. Ann. Math. Statist. 27, 887-906. Cerca con Google

21. Lindsay, B. G. (1995). Mixture Models: Theory, Geometry and Applications. IMS Monographs. Hayward, CA. Cerca con Google

22. Lv, Q. & Pitchford, J. W. (2007). Stochastic von Bertalanffy models, with applications to fish recruitment. J. Theoret. Biol. 244, 640-655. Cerca con Google

23. Marin, J., Mengersen, K. & Robert, C. (2005). Bayesian modelling and inference on mixtures of distributions. In Handbook of Statistics, D. Dey & C. Rao, eds., vol. 25. Elsevier-Sciences. Cerca con Google

24. McLachlan, G. & Peel, D. (2000). Finite mixture models. Wiley Series in Probability and Statistics: Applied Probability and Statistics. Wiley-Interscience, New York. Cerca con Google

25. Meng, X.-L. & Wong, W. H. (1996). Simulating ratios of normalizing constants via a simple identity: a theoretical exploration. Statist. Sinica 6, 831-860. Cerca con Google

26. Mengersen, K. & Robert, C. (1996). Bayesian Statistics 5, chap. Testing for mixtures: a Bayesian entropy approach. Oxford University Press,Oxford. Cerca con Google

27. Neyman, J. (1939). On a new class of “contagious” distributions, applicable in entomology and bacteriology. The Annals of Mathematical Statistics 10, 35-57. Cerca con Google

28. Pearson, K. (1893). Contributions to the mathematical theory of evolution. Journal of the Royal Statistical Society 56, 675-679. Cerca con Google

29. R Development Core Team (2006). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. Cerca con Google

30. Richardson, S. & Green, P. J. (1997). On Bayesian analysis of mixtures with an unknown number of components. J. Roy. Statist. Soc. Ser. B 59, 731-792. Cerca con Google

31. Robert, C. P. & Casella, G. (2004). Monte Carlo statistical methods. Springer Texts in Statistics. New York: Springer-Verlag, 2nd ed. Cerca con Google

32. Roeder, K. (1990). Density estimation with confidence sets exemplified by superclusters and voids in the galaxies. Journal of the American Statistical Association 85. Cerca con Google

33. Roeder, K. & Wasserman, L. (1997). Practical Bayesian density estimation using mixtures of normals. Journal of the American Statistical Association 92. Cerca con Google

34. Rothenberg, T. J. (1971). Identification in parametric models. Econometrica 39, 577-591. Cerca con Google

35. Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. Wiley Series in Probability and Mathematical Statistics: Applied Probability and Statistics. New York: John Wiley & Sons Inc. Cerca con Google

36. Rubinstein, R. Y. (1981). Simulation and the Monte Carlo method. New York: John Wiley & Sons Inc. Wiley Series in Probability and Mathematical Statistics. Cerca con Google

37. Tanner, M. A. & Wong, W. H. (1987). The calculation of posterior distributions by data augmentation. with discussion and with a reply by the authors. Journal of the American Statistical Association 82, 528-550. Cerca con Google

38. Teicher, H. (1960). On the mixture of distributions. The Annals of Mathematical Statistics 31, 55-73. Cerca con Google

39. Teicher, H. (1961). Identifiability of mixtures. The Annals of Mathematical Statistics 32, 244-248. Cerca con Google

40. Teicher, H. (1963). Identifiability of finite mixtures. The Annals of Mathematical Statistics 34, 1265-1269. Cerca con Google

41. Titterington, D. M., Smith, A. F. M. & Makov, U. E. (1985). Statistical analysis of finite mixture distributions. Wiley Series in Probability and Mathematical Statistics: Applied Probability and Statistics. Chichester: John Wiley & Sons Ltd. Cerca con Google

42. Venables, W. N. & Ripley, B. D. (1994). Modern Applied Statistics with S-Plus. New York: Springer. Cerca con Google

43. von Bertalanffy, L. (1938). A quantitative theory of organic growth. Human Biol. 10, 181-213. Cerca con Google

44. Yuan, C. & Druzdzel, M. (2005). How heavy should the tails be? In Proceedings of the Eighteenth International FLAIRS Conference (FLAIRS-05), I. Russell & Z. Markov, eds. AAAI Press/The MIT Press, Menlo Park, CA. Cerca con Google

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