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Partial Variable Selection and Its’ Applications in Biostatistics

Author

Listed:
  • Jingwen Gu

    (Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, USA)

  • Ao Yuan’s

    (Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, USA
    Department of Epidemiology and Biostatistics Section, Rehabilitation Medicine, National Institutes of Health, USA)

  • Ming T Tan

    (Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, USA)

Abstract

We propose and study a method for partial covariates selection, which only select the covariates with values fall in their effective ranges. The coefficients estimates based on the resulting data is more interpretable based on the effective covariates. This is in contrast to the existing method of variable selection, in which some variables are selected/deleted in whole.

Suggested Citation

  • Jingwen Gu & Ao Yuan’s & Ming T Tan, 2018. "Partial Variable Selection and Its’ Applications in Biostatistics," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 6(1), pages 7-12, April.
    • Handle: RePEc:adp:jbboaj:v:6:y:2018:i:1:p:7-12
    DOI: 10.19080/BBOAJ.2018.06.555678
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    References listed on IDEAS

    as
    1. Hansen M. H & Yu B., 2001. "Model Selection and the Principle of Minimum Description Length," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 746-774, June.
    2. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
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