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dc.contributor.authorSørbye, Sigrunn Holbek
dc.contributor.authorRue, Håvard
dc.date.accessioned2018-06-27T08:39:11Z
dc.date.available2018-06-27T08:39:11Z
dc.date.issued2017-05-23
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.en_US
dc.descriptionAccepted manuscript version. Published version available at <a href=https://doi.org/10.1111/jtsa.12242> https://doi.org/10.1111/jtsa.12242 </a>.en_US
dc.identifier.citationSørbye, S.H. & Rue, H. (2017). Penalised Complexity Priors for Stationary Autoregressive Processes. Journal of Time Series Analysis. 38(6), 923-935. https://doi.org/10.1111/jtsa.12242en_US
dc.identifier.issn0143-9782
dc.identifier.issn1467-9892
dc.identifier.otherFRIDAID 1471830
dc.identifier.other10.1111/jtsa.12242
dc.identifier.urihttps://hdl.handle.net/10037/13016
dc.language.isoengen_US
dc.publisherWileyen_US
dc.relation.journalJournal of Time Series Analysis
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/ISPNATTEK/239048/Norway/Institution based strategic project - Mathematics and Statistics at UiT The Arctic University of Norway//en_US
dc.rights.accessRightsopenAccessen_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Matematikk: 410en_US
dc.subjectVDP::Mathematics and natural science: 400::Mathematics: 410en_US
dc.subjectAR( p)en_US
dc.subjectlatent Gaussian modelsen_US
dc.subjectprior selectionen_US
dc.subjectR‐INLAen_US
dc.subjectrobustness. JEL. C11; C18; C22; C88en_US
dc.titlePenalised Complexity Priors for Stationary Autoregressive Processesen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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