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Spline-based nonparametric inference in general state-switching models
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dc.contributor.author | Langrock, Roland | |
dc.contributor.author | Adam, Timo | |
dc.contributor.author | Leos-Barajas, Vianey | |
dc.contributor.author | Mews, Sina | |
dc.contributor.author | Miller, David L. | |
dc.contributor.author | Papastamatiou, Yannis P. | |
dc.date.accessioned | 2020-04-21T23:33:14Z | |
dc.date.available | 2020-04-21T23:33:14Z | |
dc.date.issued | 2018-08-01 | |
dc.identifier.citation | Langrock , R , Adam , T , Leos-Barajas , V , Mews , S , Miller , D L & Papastamatiou , Y P 2018 , ' Spline-based nonparametric inference in general state-switching models ' , Statistica Neerlandica , vol. 72 , no. 3 , pp. 179-200 . https://doi.org/10.1111/stan.12133 | en |
dc.identifier.issn | 0039-0402 | |
dc.identifier.other | PURE: 253097859 | |
dc.identifier.other | PURE UUID: be5512a9-d930-4d45-a12f-f61650186545 | |
dc.identifier.other | crossref: 10.1111/stan.12133 | |
dc.identifier.other | Scopus: 85050095311 | |
dc.identifier.other | WOS: 000438904800002 | |
dc.identifier.uri | https://hdl.handle.net/10023/19836 | |
dc.description.abstract | State‐switching models combine immense flexibility with relative mathematical simplicity and computational tractability and, as a consequence, have established themselves as general‐purpose models for time series data. In this paper, we provide an overview of ways to use penalized splines to allow for flexible nonparametric inference within state‐switching models, and provide a critical discussion of the use of corresponding classes of models. The methods are illustrated using animal acceleration data and energy price data. | |
dc.format.extent | 22 | |
dc.language.iso | eng | |
dc.relation.ispartof | Statistica Neerlandica | en |
dc.rights | © 2018, the Authors, Statistica Neerlandica. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1111/stan.12133 | en |
dc.subject | Hidden Markov model | en |
dc.subject | Maximum penalized likelihood | en |
dc.subject | Markov-switching regression | en |
dc.subject | Penalized splines | en |
dc.subject | HA Statistics | en |
dc.subject | QA Mathematics | en |
dc.subject | NDAS | en |
dc.subject.lcc | HA | en |
dc.subject.lcc | QA | en |
dc.title | Spline-based nonparametric inference in general state-switching models | en |
dc.type | Journal article | en |
dc.description.version | Postprint | en |
dc.contributor.institution | University of St Andrews. School of Mathematics and Statistics | en |
dc.contributor.institution | University of St Andrews. Applied Mathematics | en |
dc.contributor.institution | University of St Andrews. Centre for Research into Ecological & Environmental Modelling | en |
dc.identifier.doi | https://doi.org/10.1111/stan.12133 | |
dc.description.status | Peer reviewed | en |
dc.date.embargoedUntil | 2020-04-22 | |
dc.identifier.url | http://www.scopus.com/inward/record.url?scp=85050095311&partnerID=8YFLogxK | en |
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