Robust penalized regression spline fitting with application to additive mixed modeling |
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Authors: | Thomas C. M. Lee Hee-Seok Oh |
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Affiliation: | (1) Department of Statistics, Colorado State University, Fort Collins, CO 80523-1877, USA;(2) Department of Statistics, Seoul National University, San 56-1, Sillim-dong, Gwanak-gu, Seoul, 151-747, South Korea |
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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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Keywords: | Additive mixed models M-type robust estimation Penalized splines Robust smoothing Semiparametric regression |
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