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Why Model Averaging?

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Model Averaging

Part of the book series: SpringerBriefs in Statistics ((BRIEFSSTATIST))

Abstract

Model averaging is a means of allowing for model uncertainty in estimation which can provide better estimates and more reliable confidence intervals than model selection. We illustrate its use via examples involving real data, discuss when it is likely to be useful, and compare the frequentist and Bayesian approaches to model averaging.

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Notes

  1. 1.

    Reprinted from: Galton, F.: Vox Populi. Nature, 75, 450–451, ©1907, with permission from Springer Nature.

  2. 2.

    An interesting discussion of Galton’s analysis, including typographical errors, use of the median rather than the mean, and the asymmetric form of the distribution of guesses, can be found in [225].

  3. 3.

    As noted later in this Chapter, we do not regard any model to be a perfect representation of the true date-generating mechanism, but behaving as if a model were true can be useful for inference.

  4. 4.

    For simplicity, throughout the book we use the term estimate when referring to either an estimator (the method of estimation) or an estimate (the realised value of an estimator), as the meaning should be clear from the context.

  5. 5.

    Some methods of model averaging, such as stacking (Sects. 2.3.2 and 3.2.3), also make use of sample-splitting.

  6. 6.

    Not to be confused with the term ensemble method, which is often used in machine learning to describe a technique for model averaging [42].

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  1. Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand

    David Fletcher

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Fletcher, D. (2018). Why Model Averaging?. In: Model Averaging. SpringerBriefs in Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-58541-2_1

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