International Statistical Review

Comparison of Sampling Schemes for Dynamic Linear Models

Edna A. Reis, Esther Salazar, and Dani Gamerman

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Abstract

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.

Article information

Source
Internat. Statist. Rev., Volume 74, Number 2 (2006), 203-214.

Dates
First available in Project Euclid: 24 July 2006

Permanent link to this document
https://projecteuclid.org/euclid.isr/1153748793

Keywords
Bayesian inference Blocking MCMC Reparameterization State space

Citation

Reis, Edna A.; Salazar, Esther; Gamerman, Dani. Comparison of Sampling Schemes for Dynamic Linear Models. Internat. Statist. Rev. 74 (2006), no. 2, 203--214. https://projecteuclid.org/euclid.isr/1153748793


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