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A Nonparametric Model for Stationary Time Series
Author(s) -
AntonianoVillalobos Isadora,
Walker Stephen G.
Publication year - 2016
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/jtsa.12146
Subject(s) - nonparametric statistics , markov chain monte carlo , series (stratigraphy) , statistical inference , markov chain , mathematics , inference , flexibility (engineering) , econometrics , mathematical optimization , algorithm , computer science , monte carlo method , statistics , artificial intelligence , paleontology , biology
Stationary processes are a natural choice as statistical models for time series data, owing to their good estimating properties. In practice, however, alternative models are often proposed that sacrifice stationarity in favour of the greater modelling flexibility required by many real‐life applications. We present a family of time‐homogeneous Markov processes with nonparametric stationary densities, which retain the desirable statistical properties for inference, while achieving substantial modelling flexibility, matching those achievable with certain non‐stationary models. A latent extension of the model enables exact inference through a trans‐dimensional Markov chain Monte Carlo method. Numerical illustrations are presented.