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• W2108146734 abstract "We present a stochastic simulation technique for subset selection in time series models, based on the use of indicator variables with the Gibbs sampler within a hierarchical Bayesian framework. As an example, the method is applied to the selection of subset linear AR models, in which only significant lags are included. Joint sampling of the indicators and parameters is found to speed convergence. We discuss the possibility of model mixing where the model is not well determined by the data, and the extension of the approach to include non-linear model terms." @default.
• W2108146734 created "2016-06-24" @default.
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• W2108146734 creator A5091167299 @default.
• W2108146734 date "2002-11-22" @default.
• W2108146734 modified "2023-09-26" @default.
• W2108146734 title "Bayesian model selection for time series using Markov chain Monte Carlo" @default.
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• W2108146734 doi "https://doi.org/10.1109/icassp.1997.604681" @default.
• W2108146734 hasPublicationYear "2002" @default.
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