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dc.contributor.authorSørbye, Sigrunn Holbek
dc.contributor.authorRue, Haavard
dc.date.accessioned2017-06-21T06:48:05Z
dc.date.available2017-06-21T06:48:05Z
dc.date.issued2017-05-23
dc.identifier.citationSørbye SH, Rue H (2017) Penalised Complexity Priors for Stationary Autoregressive Processes. Journal of Time Series Analysis 38: 923–935. Available: http://dx.doi.org/10.1111/jtsa.12242.
dc.identifier.issn0143-9782
dc.identifier.doi10.1111/jtsa.12242
dc.identifier.urihttp://hdl.handle.net/10754/625107
dc.description.abstractThe autoregressive (AR) process of order p(AR(p)) is a central model in time series analysis. A Bayesian approach requires the user to define a prior distribution for the coefficients of the AR(p) model. Although it is easy to write down some prior, it is not at all obvious how to understand and interpret the prior distribution, to ensure that it behaves according to the users' prior knowledge. In this article, we approach this problem using the recently developed ideas of penalised complexity (PC) priors. These prior have important properties like robustness and invariance to reparameterisations, as well as a clear interpretation. A PC prior is computed based on specific principles, where model component complexity is penalised in terms of deviation from simple base model formulations. In the AR(1) case, we discuss two natural base model choices, corresponding to either independence in time or no change in time. The latter case is illustrated in a survival model with possible time-dependent frailty. For higher-order processes, we propose a sequential approach, where the base model for AR(p) is the corresponding AR(p-1) model expressed using the partial autocorrelations. The properties of the new prior distribution are compared with the reference prior in a simulation study.
dc.publisherWiley
dc.relation.urlhttp://onlinelibrary.wiley.com/doi/10.1111/jtsa.12242/full
dc.rightsThis is the peer reviewed version of the following article: Penalised Complexity Priors for Stationary Autoregressive Processes, which has been published in final form at http://doi.org/10.1111/jtsa.12242. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
dc.subjectAR(p)
dc.subjectLatent Gaussian models
dc.subjectPrior selection
dc.subjectR-INLA
dc.subjectRobustness
dc.titlePenalised Complexity Priors for Stationary Autoregressive Processes
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalJournal of Time Series Analysis
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Mathematics and Statistics; UiT The Arctic University of Norway; Tromsø Norway
dc.identifier.arxividarXiv:1608.08941
kaust.personRue, Haavard
refterms.dateFOA2018-05-23T00:00:00Z
dc.date.published-online2017-05-23
dc.date.published-print2017-11


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