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A New Algorithm in Maximum Likelihood Estimation for Generalized Linear Models

Author

Listed:
  • Yufang Wen
  • Xiangdong Song
  • Haisen Zhang

Abstract

We intrduce a new algorithm for  regularized generalized linear models. The  regularization procedure is useful,especially because it ,in effect,selects variables according to the amount of penalization on the  norm of the coefficients,in a manner less greedy than forward selection/backward deletion. The algorithm efficiently computes solutions along the entire regularization path using the predictor-corrector method of convex-optimization. Selecting the step length of the regularization parameter is critical in controlling the overall accuracy of the paths; we suggest intuitive and flexible strategies for choosing appropriate values.

Suggested Citation

  • Yufang Wen & Xiangdong Song & Haisen Zhang, 2008. "A New Algorithm in Maximum Likelihood Estimation for Generalized Linear Models," Modern Applied Science, Canadian Center of Science and Education, vol. 2(5), pages 1-86, September.
    • Handle: RePEc:ibn:masjnl:v:2:y:2008:i:5:p:86
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    References listed on IDEAS

    as
    1. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    2. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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