AdaptSPEC-X: Covariate-Dependent Spectral Modeling of Multiple Nonstationary Time Series

Bertolacci, M; Rosen, O; Cripps, E; Cripps, S

AdaptSPEC-X: Covariate-Dependent Spectral Modeling of Multiple Nonstationary Time Series

Bertolacci, M Rosen, O Cripps, E Cripps, S

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Publisher:: Taylor and Francis Group
Publication Type:: Journal Article
Citation:: Journal of Computational and Graphical Statistics, 2022, 31, (2), pp. 436-454
Issue Date:: 2022

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Field	Value	Language
dc.contributor.author	Bertolacci, M
dc.contributor.author	Rosen, O
dc.contributor.author	Cripps, E
dc.contributor.author	Cripps, S https://orcid.org/0000-0003-3207-172X
dc.date.accessioned	2023-03-31T20:04:45Z
dc.date.available	2023-03-31T20:04:45Z
dc.date.issued	2022
dc.identifier.citation	Journal of Computational and Graphical Statistics, 2022, 31, (2), pp. 436-454
dc.identifier.issn	1061-8600
dc.identifier.issn	1537-2715
dc.identifier.uri	http://hdl.handle.net/10453/169036
dc.description.abstract	We present the AdaptSPEC-X method for the joint analysis of a panel of possibly nonstationary time series. The approach is Bayesian and uses a covariate-dependent infinite mixture model to incorporate multiple time series, with mixture components parameterized by a time-varying mean and log spectrum. The mixture components are based on AdaptSPEC, a nonparametric model which adaptively divides the time series into an unknown number of segments and estimates the local log spectra by smoothing splines. AdaptSPEC-X extends AdaptSPEC in three ways. First, through the infinite mixture, it applies to multiple time series linked by covariates. Second, it can handle missing values, a common feature of time series which can cause difficulties for nonparametric spectral methods. Third, it allows for a time-varying mean. Through these extensions, AdaptSPEC-X can estimate time-varying means and spectra at observed and unobserved covariate values, allowing for predictive inference. Estimation is performed by Markov chain Monte Carlo (MCMC) methods, combining data augmentation, reversible jump, and Riemann manifold Hamiltonian Monte Carlo techniques. We evaluate the methodology using simulated data, and describe applications to Australian rainfall data and measles incidence in the United States. Software implementing the method proposed in this article is available in the R package BayesSpec. Supplementary files for this article are available online.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Taylor and Francis Group
dc.relation.ispartof	Journal of Computational and Graphical Statistics
dc.relation.isbasedon	10.1080/10618600.2021.2000870
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0104 Statistics, 1403 Econometrics
dc.subject.classification	Statistics & Probability
dc.title	AdaptSPEC-X: Covariate-Dependent Spectral Modeling of Multiple Nonstationary Time Series
dc.type	Journal Article
utslib.citation.volume	31
utslib.location.activity	United States
utslib.for	0104 Statistics
utslib.for	1403 Econometrics
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Provost
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-03-31T20:04:40Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	31
utslib.citation.issue	2

Abstract:

We present the AdaptSPEC-X method for the joint analysis of a panel of possibly nonstationary time series. The approach is Bayesian and uses a covariate-dependent infinite mixture model to incorporate multiple time series, with mixture components parameterized by a time-varying mean and log spectrum. The mixture components are based on AdaptSPEC, a nonparametric model which adaptively divides the time series into an unknown number of segments and estimates the local log spectra by smoothing splines. AdaptSPEC-X extends AdaptSPEC in three ways. First, through the infinite mixture, it applies to multiple time series linked by covariates. Second, it can handle missing values, a common feature of time series which can cause difficulties for nonparametric spectral methods. Third, it allows for a time-varying mean. Through these extensions, AdaptSPEC-X can estimate time-varying means and spectra at observed and unobserved covariate values, allowing for predictive inference. Estimation is performed by Markov chain Monte Carlo (MCMC) methods, combining data augmentation, reversible jump, and Riemann manifold Hamiltonian Monte Carlo techniques. We evaluate the methodology using simulated data, and describe applications to Australian rainfall data and measles incidence in the United States. Software implementing the method proposed in this article is available in the R package BayesSpec. Supplementary files for this article are available online.

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http://hdl.handle.net/10453/169036