Bayesian model determination for binary-time-series data with applications
| Autor: | Shu-Ing Liu |
|---|---|
| Rok vydání: | 2001 |
| Předmět: |
Statistics and Probability
Nonlinear autoregressive exogenous model Markov chain Applied Mathematics Autocorrelation Markov chain Monte Carlo Computational Mathematics symbols.namesake Computational Theory and Mathematics Autoregressive model Statistics symbols Time series Marginal distribution STAR model Mathematics |
| Zdroj: | Computational Statistics & Data Analysis. 36:461-473 |
| ISSN: | 0167-9473 |
| DOI: | 10.1016/s0167-9473(00)00053-0 |
| Popis: | This article considers the Bayesian analysis of binary-time-series data. Continuous latent random variables are introduced to develop a regression model containing some exogenous variables and past experiences expressed by an autoregressive model. The order of the autoregressive component is treated as a parameter, instead of being fixed in advance. By using some reversible Markov chain Monte Carlo technique proposed by Green (Biometrika 82 (1995) 711), the order of the autoregressive component is determined, the marginal posterior density of each parameter and the multi-period predictive probabilities are respectively estimated. Simulation is provided to examine influences on the order determination, via the size of the binary time series. Also, one set of real data, the Six Cities study of the health effects of air population, is applied to illustrate the usage of the proposed model. Some interesting results about maternal smoking and child's wheezing are interpreted. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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