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Comparison of Sampling Schemes for Dynamic Linear Models
Author(s) -
Reis Edna A.,
Salazar Esther,
Gamerman Dani
Publication year - 2006
Publication title -
international statistical review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.051
H-Index - 54
eISSN - 1751-5823
pISSN - 0306-7734
DOI - 10.1111/j.1751-5823.2006.tb00170.x
Subject(s) - mathematics , volatility (finance) , sampling (signal processing) , algorithm , humanities , econometrics , computer science , philosophy , filter (signal processing) , computer vision
Summary 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.