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Risk and resampling under model uncertainty

Snigdhansu Chatterjee and Nitai D. Mukhopadhyay

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In statistical exercises where there are several candidate models, the traditional approach is to select one model using some data driven criterion and use that model for estimation, testing and other purposes, ignoring the variability of the model selection process. We discuss some problems associated with this approach. An alternative scheme is to use a model-averaged estimator, that is, a weighted average of estimators obtained under different models, as an estimator of a parameter. We show that the risk associated with a Bayesian model-averaged estimator is bounded as a function of the sample size, when parameter values are fixed. We establish conditions which ensure that a model-averaged estimator’s distribution can be consistently approximated using the bootstrap. A new, data-adaptive, model averaging scheme is proposed that balances efficiency of estimation without compromising applicability of the bootstrap. This paper illustrates that certain desirable risk and resampling properties of model-averaged estimators are obtainable when parameters are fixed but unknown; this complements several studies on minimaxity and other properties of post-model-selected and model-averaged estimators, where parameters are allowed to vary.

Chapter information

Bertrand Clarke and Subhashis Ghosal, eds., Pushing the Limits of Contemporary Statistics: Contributions in Honor of Jayanta K. Ghosh (Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2008), 155-169

First available in Project Euclid: 28 April 2008

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Digital Object Identifier

Mathematical Reviews number (MathSciNet)

Primary: 60F12
Secondary: 60J05: Discrete-time Markov processes on general state spaces 62C10: Bayesian problems; characterization of Bayes procedures 62F40: Bootstrap, jackknife and other resampling methods

bootstrap bounded risk linear regression model averaging model selection

Copyright © 2008, Institute of Mathematical Statistics


Chatterjee, Snigdhansu; Mukhopadhyay, Nitai D. Risk and resampling under model uncertainty. Pushing the Limits of Contemporary Statistics: Contributions in Honor of Jayanta K. Ghosh, 155--169, Institute of Mathematical Statistics, Beachwood, Ohio, USA, 2008. doi:10.1214/074921708000000129.

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