On Parametric Bootstrapping and Bayesian Prediction
We investigate bootstrapping and Bayesian methods for prediction. The observations and the variable being predicted are distributed according to different distributions. Many important problems can be formulated in this setting. This type of prediction problem appears when we deal with a Poisson pro...
| Veröffentlicht in: | Scandinavian Journal of Statistics. - Blackwell Publishers, 1974. - 31(2004), 3, Seite 403-416 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2004
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| Zugriff auf das übergeordnete Werk: | Scandinavian Journal of Statistics |
| Schlagworte: | asymptotic theory Bayesian prediction bootstrap predictive distribution information geometry Kullback-Leibler divergence Mathematics Applied sciences Philosophy Behavioral sciences |
| Zusammenfassung: | We investigate bootstrapping and Bayesian methods for prediction. The observations and the variable being predicted are distributed according to different distributions. Many important problems can be formulated in this setting. This type of prediction problem appears when we deal with a Poisson process. Regression problems can also be formulated in this setting. First, we show that bootstrap predictive distributions are equivalent to Bayesian predictive distributions in the second-order expansion when some conditions are satisfied. Next, the performance of predictive distributions is compared with that of a plug-in distribution with an estimator. The accuracy of prediction is evaluated by using the Kullback-Leibler divergence. Finally, we give some examples. |
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| ISSN: | 14679469 |