Here we summarize some results that are further developed in Nava et al. (2013). We pro-pose a method to construct strictly stationary Markovian models with fixed invariant distributions. Of particular interest are those models with invariant distributions belonging to the class of Generalized Inverse Gaussian (GIG) distributions family. The construction we propose is based on a Poisson trans-form which controls the dependence structure in the model. In particular, it allows to fully control the underlying transition probabilities that, an appealing feature, is then incorporated within standard es-timation methods. A Bayesian estimate via a Gibbs sampler algorithm, based on the slice method, is proposed and implemented.

On Stationary Markov Models: a Poisson-driven approach

PRUENSTER, IGOR
2013

Abstract

Here we summarize some results that are further developed in Nava et al. (2013). We pro-pose a method to construct strictly stationary Markovian models with fixed invariant distributions. Of particular interest are those models with invariant distributions belonging to the class of Generalized Inverse Gaussian (GIG) distributions family. The construction we propose is based on a Poisson trans-form which controls the dependence structure in the model. In particular, it allows to fully control the underlying transition probabilities that, an appealing feature, is then incorporated within standard es-timation methods. A Bayesian estimate via a Gibbs sampler algorithm, based on the slice method, is proposed and implemented.
2013
9788864930190
Proceedings of the 8th Conference on Statistical Computing and Complex Systems - SCo 2013
C., Nava; R. H., Mena; Pruenster, Igor
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11565/3989948
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