Journal of the Korean Data and Information Science Society

한국데이터정보과학회 (The Korean Data and Information Science Society)
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Estimation and variable selection in censored regression model with smoothly clipped absolute deviation penalty

  • Shim, Jooyong (Department of Statistics, Institute of Statistical Information, Inje University) ;
  • Bae, Jongsig (Department of Mathematics, Sungkyunkwan University) ;
  • Seok, Kyungha (Department of Statistics, Institute of Statistical Information, Inje University)
  • 투고 : 2016.09.28
  • 심사 : 2016.11.23
  • 발행 : 2016.11.30
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초록

Smoothly clipped absolute deviation (SCAD) penalty is known to satisfy the desirable properties for penalty functions like as unbiasedness, sparsity and continuity. In this paper, we deal with the regression function estimation and variable selection based on SCAD penalized censored regression model. We use the local linear approximation and the iteratively reweighted least squares algorithm to solve SCAD penalized log likelihood function. The proposed method provides an efficient method for variable selection and regression function estimation. The generalized cross validation function is presented for the model selection. Applications of the proposed method are illustrated through the simulated and a real example.

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참고문헌

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피인용 문헌

  1. 사례연구: 대구 파티마 병원 폐렴 입원 환자 수에 영향을 미치는 날씨 변수 선택 vol.28, pp.1, 2016, https://doi.org/10.7465/jkdi.2017.28.1.131