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Robust penalized regression spline fitting with application to additive mixed modeling

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Abstract

An increasingly popular method for smoothing noisy data is penalized regression spline fitting. In this paper a new procedure is proposed for fitting robust penalized regression splines. This procedure is computationally fast, straightforward to implement, and can be paired with any smoothing parameter selection method. In addition, it can also be extended to other settings, such as additive mixed modeling. Both simulated and real data examples are used to illustrate the effectiveness of the procedure.

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Authors and Affiliations

  1. Department of Statistics, Colorado State University, Fort Collins, CO, 80523-1877, USA

    Thomas C. M. Lee

  2. Department of Statistics, Seoul National University, San 56-1, Sillim-dong, Gwanak-gu, Seoul, 151-747, South Korea

    Hee-Seok Oh

Authors
  1. Thomas C. M. Lee
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  2. Hee-Seok Oh
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Correspondence to Hee-Seok Oh.

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Lee, T.C.M., Oh, HS. Robust penalized regression spline fitting with application to additive mixed modeling. Computational Statistics 22, 159–171 (2007). https://doi.org/10.1007/s00180-007-0031-6

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  • DOI: https://doi.org/10.1007/s00180-007-0031-6

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