Spline-based nonparametric inference in general state-switching models

Roland Langrock*, Timo Adam, Vianey Leos-Barajas, Sina Mews, David L. Miller, Yannis P. Papastamatiou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)179-200
Number of pages22
JournalStatistica Neerlandica
Volume72
Issue number3
Early online date22 Apr 2018
DOIs
Publication statusPublished - 1 Aug 2018
Event32nd International Workshop on Statistical Modelling (IWSM) - Groningen, Netherlands
Duration: 1 Jul 2017 → …

Keywords

  • Hidden Markov model
  • Maximum penalized likelihood
  • Markov-switching regression
  • Penalized splines

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