Accounting for Model Uncertainty in Seemingly Unrelated Regressions
This article considers inference in a Bayesian seemingly unrelated regression (SUR) model where the set of regressors is assumed unknown a priori. That is, we allow for uncertainty in the covariate set by defining a prior distribution on the model space. The posterior inference is analytically intra...
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| Veröffentlicht in: | Journal of computational and graphical statistics 2002-09, Vol.11 (3), p.533-551 |
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| container_end_page | 551 |
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| container_issue | 3 |
| container_start_page | 533 |
| container_title | Journal of computational and graphical statistics |
| container_volume | 11 |
| creator | Holmes, C. C Denison, D. G. T Mallick, B. K |
| description | This article considers inference in a Bayesian seemingly unrelated regression (SUR) model where the set of regressors is assumed unknown a priori. That is, we allow for uncertainty in the covariate set by defining a prior distribution on the model space. The posterior inference is analytically intractable and we adopt computer-intensive simulation using variable dimension Markov chain Monte Carlo algorithms to approximate quantities of interest. Applications are given for vector autoregression (VAR) models of unknown order and multivariate spline models with unknown knot points. |
| doi_str_mv | 10.1198/106186002475 |
| format | Article |
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| ispartof | Journal of computational and graphical statistics, 2002-09, Vol.11 (3), p.533-551 |
| issn | 1061-8600 1537-2715 |
| language | eng |
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| source | JSTOR Mathematics & Statistics; Jstor Complete Legacy |
| subjects | Bayesian model choice Curve fitting Data smoothing Datasets Linear regression Markov chain Monte Carlo Markov chains Modeling Multivariate regression Parametric models Regression analysis Time series models Vector autoregression |
| title | Accounting for Model Uncertainty in Seemingly Unrelated Regressions |
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