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Error analysis for coefficient-based regularized regression in additive models

Author

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  • Tao, Yanfang
  • Song, Biqin
  • Li, Luoqing

Abstract

This paper considers a coefficient-based additive model with the ℓq-regularizer (1≤q≤2). Error bounds are established for the proposed model by integrating the stepping stone technique and the concentration estimate with empirical covering numbers. From error analysis, we obtain a sharp learning rate that can be arbitrarily close to O(nϵ−1) under mild conditions.

Suggested Citation

  • Tao, Yanfang & Song, Biqin & Li, Luoqing, 2018. "Error analysis for coefficient-based regularized regression in additive models," Statistics & Probability Letters, Elsevier, vol. 134(C), pages 22-28.
    • Handle: RePEc:eee:stapro:v:134:y:2018:i:c:p:22-28
    DOI: 10.1016/j.spl.2017.10.001
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    References listed on IDEAS

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    1. Christmann, Andreas & Hable, Robert, 2012. "Consistency of support vector machines using additive kernels for additive models," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 854-873.
    2. Pradeep Ravikumar & John Lafferty & Han Liu & Larry Wasserman, 2009. "Sparse additive models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(5), pages 1009-1030, November.
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