Title
Interweaving Markov Chain Monte Carlo Strategies for Efficient Estimation of Dynamic Linear Models
Campus Units
Statistics
Document Type
Article
Publication Version
Accepted Manuscript
Publication Date
2-16-2017
Journal or Book Title
Journal of Computational and Graphical Statistics
Volume
26
First Page
152
Last Page
159
DOI
10.1080/10618600.2015.1105748
Abstract
In dynamic linear models (DLMs) with unknown fixed parameters, a standard Markov chain Monte Carlo (MCMC) sampling strategy is to alternate sampling of latent states conditional on fixed parameters and sampling of fixed parameters conditional on latent states. In some regions of the parameter space, this standard data augmentation (DA) algorithm can be inefficient. To improve efficiency, we apply the interweaving strategies of Yu and Meng to DLMs. For this, we introduce three novel alternative DAs for DLMs: the scaled errors, wrongly scaled errors, and wrongly scaled disturbances. With the latent states and the less well known scaled disturbances, this yields five unique DAs to employ in MCMC algorithms. Each DA implies a unique MCMC sampling strategy and they can be combined into interweaving and alternating strategies that improve MCMC efficiency. We assess these strategies using the local level model and demonstrate that several strategies improve efficiency relative to the standard approach and the most efficient strategy interweaves the scaled errors and scaled disturbances. Supplementary materials are available online for this article.
Copyright Owner
Taylor & Francis
Copyright Date
2017
Language
en
File Format
application/pdf
Recommended Citation
Simpson, Matthew; Niemi, Jarad; and Roy, Vivekananda, "Interweaving Markov Chain Monte Carlo Strategies for Efficient Estimation of Dynamic Linear Models" (2017). Statistics Publications. 89.
https://lib.dr.iastate.edu/stat_las_pubs/89
Comments
This is a manuscript of an article from Journal of Computational and Graphical Statistics 26 (2017): 152, doi: 10.1080/10618600.2015.1105748. Posted with permission.