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Spline-based nonparametric inference in general state-switching models

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statneer_hmm.pdf (1.201Mb)
Date
01/08/2018
Author
Langrock, Roland
Adam, Timo
Leos-Barajas, Vianey
Mews, Sina
Miller, David L.
Papastamatiou, Yannis P.
Keywords
Hidden Markov model
Maximum penalized likelihood
Markov-switching regression
Penalized splines
HA Statistics
QA Mathematics
NDAS
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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.
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
Publication
Statistica Neerlandica
Status
Peer reviewed
DOI
https://doi.org/10.1111/stan.12133
ISSN
0039-0402
Type
Journal article
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
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  • University of St Andrews Research
URL
http://www.scopus.com/inward/record.url?scp=85050095311&partnerID=8YFLogxK
URI
http://hdl.handle.net/10023/19836

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