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Non‐linear autoregressive time series with multivariate Gaussian mixtures as marginal distributions
Author(s) -
Glasbey C. A.
Publication year - 2001
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/1467-9876.00225
Subject(s) - autoregressive model , multivariate statistics , star model , series (stratigraphy) , gaussian , mathematics , marginal distribution , econometrics , autoregressive integrated moving average , time series , setar , statistics , linear model , random variable , geology , physics , quantum mechanics , paleontology
A new form of non‐linear autoregressive time series is proposed to model solar radiation data, by specifying joint marginal distributions at low lags to be multivariate Gaussian mixtures. The model is also a type of multiprocess dynamic linear model, but with the advantage that the likelihood has a closed form.