Computational Statistics

, Volume 22, Issue 1, pp 159–171

Robust penalized regression spline fitting with application to additive mixed modeling

Original Paper

DOI: 10.1007/s00180-007-0031-6

Cite this article as:
Lee, T.C.M. & Oh, HS. Computational Statistics (2007) 22: 159. doi:10.1007/s00180-007-0031-6

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.

Keywords

Additive mixed models M-type robust estimation Penalized splines Robust smoothing Semiparametric regression 

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  1. 1.Department of StatisticsColorado State UniversityFort CollinsUSA
  2. 2.Department of StatisticsSeoul National UniversityGwanak-gu, SeoulSouth Korea

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