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FSR methods for second-order regression models

Author

Listed:
  • Crews, Hugh B.
  • Boos, Dennis D.
  • Stefanski, Leonard A.

Abstract

Most variable selection techniques focus on first-order linear regression models. Often, interaction and quadratic terms are also of interest, but the number of candidate predictors grows very fast with the number of original predictors, making variable selection more difficult. Forward selection algorithms are thus developed that enforce natural hierarchies in second-order models to control the entry rate of uninformative effects and to equalize the false selection rates from first-order and second-order terms. Method performance is compared through Monte Carlo simulation and illustrated with data from a Cox regression and from a response surface experiment.

Suggested Citation

  • Crews, Hugh B. & Boos, Dennis D. & Stefanski, Leonard A., 2011. "FSR methods for second-order regression models," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2026-2037, June.
    • Handle: RePEc:eee:csdana:v:55:y:2011:i:6:p:2026-2037
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    References listed on IDEAS

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    1. Wu, Yujun & Boos, Dennis D. & Stefanski, Leonard A., 2007. "Controlling Variable Selection by the Addition of Pseudovariables," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 235-243, March.
    2. Dennis D. Boos & Leonard A. Stefanski & Yujun Wu, 2009. "Fast FSR Variable Selection with Applications to Clinical Trials," Biometrics, The International Biometric Society, vol. 65(3), pages 692-700, September.
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    Cited by:

    1. Jonathan Boss & Alexander Rix & Yin‐Hsiu Chen & Naveen N. Narisetty & Zhenke Wu & Kelly K. Ferguson & Thomas F. McElrath & John D. Meeker & Bhramar Mukherjee, 2021. "A hierarchical integrative group least absolute shrinkage and selection operator for analyzing environmental mixtures," Environmetrics, John Wiley & Sons, Ltd., vol. 32(8), December.
    2. Zak-Szatkowska, Malgorzata & Bogdan, Malgorzata, 2011. "Modified versions of the Bayesian Information Criterion for sparse Generalized Linear Models," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 2908-2924, November.

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