Bootstrap Information Criterion

Part of the Springer Series in Statistics book series (SSS)

Advances in computing now allow numerical methods to be used for modeling complex systems, instead of analytic methods. Complex Bayesian models can now be used for practical applications by using numerical methods such as the Markov chain Monte Carlo (MCMC) technique. Also, when the maximum likelihood estimator cannot be obtained analytically, it is possible to obtain it by a numerical optimization method. In conjunction with the development of numerical methods, model evaluation must now deal with extremely complex and increasingly diverse models. The bootstrap information criterion [Efron (1983), Wong (1983), Konishi and Kitagawa (1996), Ishiguro et al. (1997), Cavanaugh and Shumway (1997), and Shibata (1997)], obtained by applying the bootstrap methods originally proposed by Efron (1979), permits the evaluation of models estimated through complex processes.


Bootstrap Method Bootstrap Sample Bias Correction Variance Reduction True Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer Science+Business Media, LLC 2008

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