Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-12-03T19:16:51.741Z Has data issue: false hasContentIssue false

Conditionally identically distributed species sampling sequences

Published online by Cambridge University Press:  01 July 2016

Federico Bassetti*
Affiliation:
University of Pavia
Irene Crimaldi*
Affiliation:
University of Bologna
Fabrizio Leisen*
Affiliation:
University of Navarra
*
Postal address: Department of Mathematics, University of Pavia, via Ferrata 1, 27100 Pavia, Italy. Email address: [email protected]
∗∗ Postal address: Department of Mathematics, University of Bologna, Piazza di Porta San Donato 5, 40126 Bologna, Italy. Email address: [email protected]
∗∗∗ Postal address: Faculty of Economics, University of Navarra, Campus Universitario, Edificio de Biblioteca (Entrada Este), 31008, Pamplona, Spain. Email address: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

In this paper the theory of species sampling sequences is linked to the theory of conditionally identically distributed sequences in order to enlarge the set of species sampling sequences which are mathematically tractable. The conditional identity in distribution (see Berti, Pratelli and Rigo (2004)) is a new type of dependence for random variables, which generalizes the well-known notion of exchangeability. In this paper a class of random sequences, called generalized species sampling sequences, is defined and a condition to have conditional identity in distribution is given. Moreover, two types of generalized species sampling sequence that are conditionally identically distributed are introduced and studied: the generalized Poisson-Dirichlet sequence and the generalized Ottawa sequence. Some examples are discussed.

Type
General Applied Probability
Copyright
Copyright © Applied Probability Trust 2010 

References

Aldous, D. J. (1985). Exchangeability and related topics. In École d'Été de Probabilités de Saint-Flour, XIII-1983 (Lecture Notes Math. 1117), Springer, Berlin, pp. 1198.Google Scholar
Aldous, D. J. and Eagleson, G. K. (1978). On mixing and stability of limit theorems. Ann. Prob. 6, 325331.Google Scholar
Aletti, G., May, C. and Secchi, P. (2009). A central limit theorem, and related results, for a two-color randomly reinforced urn. Adv. Appl. Prob. 41, 829844.Google Scholar
Bai, Z.-D. and Hu, F. (2005). Asymptotics in randomized URN models. Ann. Appl. Prob. 15, 914940.Google Scholar
Bassetti, F., Crimaldi, I. and Leisen, F. (2008). Conditionally identically distributed species sampling sequences. Preprint. Available at http://arxiv.org/abs/0806.2724.Google Scholar
Berti, P., Pratelli, L. and Rigo, P. (2004). Limit theorems for a class of identically distributed random variables. Ann. Prob. 32, 20292052.Google Scholar
Berti, P., Crimaldi, I., Pratelli, L. and Rigo, P. (2009). A central limit theorem and its applications to multicolor randomly reinforced urns. Preprint. Available at http://arxiv.org/abs/0904.0932.Google Scholar
Blackwell, D. and MacQueen, J. B. (1973). Ferguson distributions via Pólya urn schemes. Ann. Statist. 1, 353355.CrossRefGoogle Scholar
Crimaldi, I. (2009). An almost sure conditional convergence result and an application to a generalized Pólya urn. Internat. Math. Forum 4, 11391156.Google Scholar
Crimaldi, I. and Leisen, F. (2008). Asymptotic results for a generalized Pólya urn with ‘multi-updating’ and applications to clinical trials. Commun. Statist. Theory Meth. 37, 27772794.CrossRefGoogle Scholar
Crimaldi, I., Letta, G. and Pratelli, L. (2007). A strong form of stable convergence. In Séminaire de Probabilités XL (Lecture Notes Math. 1899), Springer, Berlin, pp. 203225.CrossRefGoogle Scholar
Ferguson, T. S. (1973). A Bayesian analysis of some nonparametric problems. Ann. Statist. 1, 209230.Google Scholar
Hall, P. and Heyde, C. C. (1980). Martingale Limit Theory and Its Application. Academic press, New York.Google Scholar
Hansen, B. and Pitman, J. (2000). Prediction rules for exchangeable sequences related to species sampling. Statist. Prob. Lett. 46, 251256.Google Scholar
Jacod, J. and Mémin, J. (1981). Sur un type de convegence intermédiaire entre la convergence en loi et la convergence en probabilité. In Séminaire de Probabilités XV (Lecture Notes Math. 850), Springer, Berlin, pp. 529546.Google Scholar
Janson, S. (2006). Limit theorems for triangular urn schemes. Prob. Theory Relat. Fields 134, 417452.Google Scholar
May, C. and Flournoy, N. (2009). Asymptotics in response-adaptive designs generated by a two-color, randomly reinforced urn. Ann. Statist. 37, 10581078.CrossRefGoogle Scholar
May, C., Paganoni, A. and Secchi, P. (2005). On a two-color generalized Pólya urn. Metron 63, 115134.Google Scholar
Pemantle, R. (2007). A survey of random processes with reinforcement. Prob. Surveys 4, 179.Google Scholar
Pitman, J. (1996). Some developments of the Blackwell–MacQueen urn scheme. In Statistics, Probability and Game Theory (IMS Lecture Notes Monogr. Ser. 30), eds Ferguson, T. S. et al., Institute of Mathematical Statistics, Hayward, CA, pp. 245267.Google Scholar
Pitman, J. (2006). Combinatorial Stochastic Processes (Lecture Notes Math. 1875), Springer, Berlin.Google Scholar
Pitman, J. and Yor, M. (1997). The two-parameter Poisson–Dirichlet distribution derived from a stable subordinator. Ann. Prob. 25, 855900.Google Scholar
Rényi, A. (1963). On stable sequences of events. Sankhyā A 25, 293302.Google Scholar
Williams, D. (1991). Probability with Martingales. Cambridge University Press.Google Scholar