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Model Selection

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Handbook of Computational Statistics

Part of the book series: Springer Handbooks of Computational Statistics ((SHCS))

Abstract

The need for model selection arises when a data-based choice among competing models has to be made. For example, for fitting parametric regression (linear, non-linear and generalized linear) models with multiple independent variables, one needs to decide which variables to include in the model (Chaps. III.7, III.8 and III.12); for fitting non-parametric regression (spline, kernel, local polynomial) models, one needs to decide the amount of smoothing (Chaps.III.5 andIII.10); for unsupervised learning, one needs to decide the number of clusters (Chaps. III.13 andIII.16); and for tree-based regression and classification, one needs to decide the size of a tree (Chap.III.14).

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Acknowledgements

This work was supported by NIH Grants R01 GM58533.

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Authors and Affiliations

  1. Department of Statistics and Applied Probability, University of California, Santa Barbara, CA, USA

    Yuedong Wang

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  1. Yuedong Wang
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Correspondence to Yuedong Wang .

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Editors and Affiliations

  1. Dept. Computational & Data, Sciences, George Mason University, University Drive 4400, Fairfax, 22030-4444, Virginia, USA

    James E. Gentle

  2. L.v.Bortkiewicz Chair of Statistics, C.A.S.E. Centre f. Appl. Stat. & Econ., Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin, 10099, Germany

    Wolfgang Karl Härdle

  3. Dept. Socioinformation, Okayama University, Ridai-cho 1-1, Okayama, 700-0005, Japan

    Yuichi Mori

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Wang, Y. (2012). Model Selection. In: Gentle, J., Härdle, W., Mori, Y. (eds) Handbook of Computational Statistics. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21551-3_16

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