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Model Averaging for High-Dimensional Linear Models

Author

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  • Juming Pan

    (Department of Mathematics and Statistics, University of Minnesota Duluth, USA)

Abstract

Model averaging has attracted increasing attention in recent years for the analysis of high-dimensional data. By weighting several competing scientific models suitably, model averaging attempts to achieve stable and improved prediction in the case where the number of predictors greatly exceeds the sample size.

Suggested Citation

  • Juming Pan, 2018. "Model Averaging for High-Dimensional Linear Models," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 6(2), pages 46-47, April.
    • Handle: RePEc:adp:jbboaj:v:6:y:2018:i:2:p:46-47
    DOI: 10.19080/BBOAJ.2018.06.555684
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    References listed on IDEAS

    as
    1. Tomohiro Ando & Ker-Chau Li, 2014. "A Model-Averaging Approach for High-Dimensional Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 254-265, March.
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