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Stationary autoregressive models via a Bayesian nonparametric approach. (English) Zbl 1097.62084

A new technique for construction of Markov time series with given marginal distribution is considered. It is based on a nonparametric Bayesian approach with a logistic normal (generalized log-Gaussian) process as a prior for the generation of a one-step ahead predictive density. This technique can be used for construction of AR(1) and discrete autoregressive (DAR(1)) processes. A stationary AR(1) process with beta margins is considered as an example. Application to wind speed data is discussed.

MSC:

62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
62F15 Bayesian inference
62G99 Nonparametric inference

Software:

sm; Ox
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References:

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