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한국통계학회> CSAM(Communications for Statistical Applications and Methods)> An efficient algorithm for the non-convex penalized multinomial logistic regression

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An efficient algorithm for the non-convex penalized multinomial logistic regression

Sunghoon Kwon , Dongshin Kim , Sangin Lee
  • : 한국통계학회
  • : CSAM(Communications for Statistical Applications and Methods) 27권1호
  • : 연속간행물
  • : 2020년 01월
  • : 129-140(12pages)

DOI


목차

1. Introduction
2. Non-convex penalized multinomial logistic regression
3. Computational algorithm
4. Numerical studies
5. Concluding remarks
Acknowledgements
References

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초록 보기


						
In this paper, we introduce an efficient algorithm for the non-convex penalized multinomial logistic regression that can be uniformly applied to a class of non-convex penalties. The class includes most non-convex penalties such as the smoothly clipped absolute deviation, minimax concave and bridge penalties. The algorithm is developed based on the concave-convex procedure and modified local quadratic approximation algorithm. However, usual quadratic approximation may slow down computational speed since the dimension of the Hessian matrix depends on the number of categories of the output variable. For this issue, we use a uniform bound of the Hessian matrix in the quadratic approximation. The algorithm is available from the R package ncpen developed by the authors. Numerical studies via simulations and real data sets are provided for illustration.

UCI(KEPA)

간행물정보

  • : 자연과학분야  > 통계학
  • : KCI등재
  • :
  • : 격월
  • : 2287-7843
  • :
  • : 학술지
  • : 연속간행물
  • : 1994-2020
  • : 1892


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저자 : Sunghoon Kwon , Dongshin Kim , Sangin Lee

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 27권 1호 발행 연도 : 2020 페이지 : pp. 129-140 (12 pages)

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33권 2호 ~ 33권 2호

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26권 6호 ~ 26권 6호

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