Spline‐based nonparametric inference in general state‐switching models. (22nd April 2018)

• Record Type: Journal Article
• Authors: Langrock, Roland; Adam, Timo; Leos‐Barajas, Vianey; Mews, Sina; Miller, David L.; Papastamatiou, Yannis P.
• Other Names: Grzegorczyk Marco guestEditor.
• Is Part Of: Statistica Neerlandica. Volume 72:Number 3(2018)
• Page Start: 179
• Page End: 200
• Publication Date: 2018-04-22
• Subjects: hidden Markov model -- maximum penalized likelihood -- Markov‐switching regression -- penalized splines; Statistics -- Periodicals; 519.5; 314.92
• DOI: 10.1111/stan.12133
• Languages: English
• ISSNs: 0039-0402

Abstract

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.