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Support vector regression with penalized likelihood

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  • Uemoto, Takumi
  • Naito, Kanta

Abstract

This paper is concerned with the method of support vector regression (SVR) with penalized likelihood. The ε-insensitive loss function utilized in SVR is naturally incorporated into the likelihood and is combined with the penalty for the vector of regression coefficients. We include all parameters necessary to implement SVR in the proposed penalized likelihood. An efficient algorithm to obtain estimators of parameters is provided and asymptotic results for the estimators are developed. We perform Monte Carlo simulations to confirm the effectiveness of the proposed method and report the results of applying the method to real data sets.

Suggested Citation

  • Uemoto, Takumi & Naito, Kanta, 2022. "Support vector regression with penalized likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    • Handle: RePEc:eee:csdana:v:174:y:2022:i:c:s0167947322001025
    DOI: 10.1016/j.csda.2022.107522
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    References listed on IDEAS

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    1. Newey, Whitney K, 1991. "Uniform Convergence in Probability and Stochastic Equicontinuity," Econometrica, Econometric Society, vol. 59(4), pages 1161-1167, July.
    2. Cantoni E. & Ronchetti E., 2001. "Robust Inference for Generalized Linear Models," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1022-1030, September.
    3. Pollard, David, 1991. "Asymptotics for Least Absolute Deviation Regression Estimators," Econometric Theory, Cambridge University Press, vol. 7(2), pages 186-199, June.
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    Cited by:

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    2. Horng, Shih-Cheng & Lin, Shieh-Shing, 2024. "Advanced golden jackal optimization for solving the constrained integer stochastic optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 217(C), pages 188-201.

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