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Smoothing and mixed models

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  • M. P. Wand

    (Harvard University)

Abstract

Summary Smoothing methods that use. basis functions with penalisation can be formulated as maximum likelihood estimators and best predictors in a mixed model framework. Such connections are at least a quarter of a century old but, perhaps with the advent of mixed model software, have led to a paradigm shift in the field of smoothing. The reason is that most, perhaps all, models involving smoothing can be expressed as a mixed model and hence enjoy the benefit of the growing body of methodology and software for general mixed model analysis. The handling of other complications such as clustering, missing data and measurement error is generally quite straightforward with mixed model representations of smoothing.

Suggested Citation

  • M. P. Wand, 2003. "Smoothing and mixed models," Computational Statistics, Springer, vol. 18(2), pages 223-249, July.
    • Handle: RePEc:spr:compst:v:18:y:2003:i:2:d:10.1007_s001800300142
    DOI: 10.1007/s001800300142
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    References listed on IDEAS

    as
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