Comparison of Sampling Schemes for Dynamic Linear Models
Reis, Edna A. ; Salazar, Esther ; Gamerman, Dani
Internat. Statist. Rev., Tome 74 (2006) no. 1, p. 203-214 / Harvested from Project Euclid
Hyperparameter estimation in dynamic linear models leads to inference that is not available analytically. Recently, the most common approach is through MCMC approximations. A number of sampling schemes that have been proposed in the literature are compared. They basically differ in their blocking structure. In this paper, comparison between the most common schemes is performed in terms of different efficiency criteria, including efficiency ratio and processing time. A sample of time series was simulated to reflect different relevant features such as series length and system volatility.
Publié le : 2006-08-14
Classification:  Bayesian inference,  Blocking,  MCMC,  Reparameterization,  State space
@article{1153748793,
     author = {Reis, Edna A. and Salazar, Esther and Gamerman, Dani},
     title = {Comparison of Sampling Schemes for Dynamic Linear Models},
     journal = {Internat. Statist. Rev.},
     volume = {74},
     number = {1},
     year = {2006},
     pages = { 203-214},
     language = {en},
     url = {http://dml.mathdoc.fr/item/1153748793}
}
Reis, Edna A.; Salazar, Esther; Gamerman, Dani. Comparison of Sampling Schemes for Dynamic Linear Models. Internat. Statist. Rev., Tome 74 (2006) no. 1, pp.  203-214. http://gdmltest.u-ga.fr/item/1153748793/