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    Cox proportional hazard model with L1 penalty

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    https://www.riss.kr/link?id=A104238169

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    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    The proposed method is based on a penalized log partial likelihood of Cox proportional hazard model with L1-penalty. We use the iteratively reweighted least squares procedure to solve L1 penalized log partial likelihood function of Cox proportional hazard model. It provide the ecient computation including variable selection and leads to the generalized cross validation function for the model selection. Experimental results are then presented to indicate the performance of the proposed procedure.
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    The proposed method is based on a penalized log partial likelihood of Cox proportional hazard model with L1-penalty. We use the iteratively reweighted least squares procedure to solve L1 penalized log partial likelihood function of Cox proportional ha...

    The proposed method is based on a penalized log partial likelihood of Cox proportional hazard model with L1-penalty. We use the iteratively reweighted least squares procedure to solve L1 penalized log partial likelihood function of Cox proportional hazard model. It provide the ecient computation including variable selection and leads to the generalized cross validation function for the model selection. Experimental results are then presented to indicate the performance of the proposed procedure.

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    참고문헌 (Reference)

    1 Tibshirani, R, "The lasso method for variable selection in the Cox model" 16 : 385-395, 1997

    2 Moody, J. E., "The effective number of parameters: An analysis of generalisation and regularisation in nonlinear learning systems, In Advances in Neural Information Processing Systems 4" 847-854, 1992

    3 Krishnapuram, B., "Sparse multinomial logistic regression: Fast algorithms and generalization bounds" 27 : 957-968, 2005

    4 Craven, P, "Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation" 31 : 377-403, 1979

    5 황창하, "Semiparametric support vector machine for accelerated failure time model" 한국데이터정보과학회 21 (21): 765-775, 2010

    6 Tibshirani, R, "Regression shrinkage and selection via the lasso" 58 : 267-288, 1996

    7 Cox, D. R., "Regression models and life tables (with discussions)" 7 : 187-220, 1972

    8 Cox, D. R., "Partial likelihood" 62 : 269-276, 1975

    9 심주용, "Kernel method for autoregressive data" 한국데이터정보과학회 20 (20): 949-954, 2009

    10 석경하, "Doubly penalized kernel method for heteroscedastic autoregressive data" 한국데이터정보과학회 21 (21): 155-162, 2010

    1 Tibshirani, R, "The lasso method for variable selection in the Cox model" 16 : 385-395, 1997

    2 Moody, J. E., "The effective number of parameters: An analysis of generalisation and regularisation in nonlinear learning systems, In Advances in Neural Information Processing Systems 4" 847-854, 1992

    3 Krishnapuram, B., "Sparse multinomial logistic regression: Fast algorithms and generalization bounds" 27 : 957-968, 2005

    4 Craven, P, "Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation" 31 : 377-403, 1979

    5 황창하, "Semiparametric support vector machine for accelerated failure time model" 한국데이터정보과학회 21 (21): 765-775, 2010

    6 Tibshirani, R, "Regression shrinkage and selection via the lasso" 58 : 267-288, 1996

    7 Cox, D. R., "Regression models and life tables (with discussions)" 7 : 187-220, 1972

    8 Cox, D. R., "Partial likelihood" 62 : 269-276, 1975

    9 심주용, "Kernel method for autoregressive data" 한국데이터정보과학회 20 (20): 949-954, 2009

    10 석경하, "Doubly penalized kernel method for heteroscedastic autoregressive data" 한국데이터정보과학회 21 (21): 155-162, 2010

    11 Breslow, N., "Covariance analysis of censored survival data" 30 : 89-99, 1974

    12 심주용, "Censored Kernel Ridge Regression" 한국데이터정보과학회 16 (16): 1045-1052, 2005

    13 Williams, P. M., "Bayesian regularization and pruning using a Laplace prior" 7 : 117-143, 1995

    14 Zhang, H. H., "Adaptive lasso for Cox's proportional hazards model" 94 : 691-703, 2007

    15 Tsiatis, R., "A heuristic estimate of the asymptotic variance of survival probability in Cox' regression model" Technical report of University of Wisconsin 1978

    16 Sauerbrei, W, "A bootstrap resampling procedure for model building: Appli-cation to the Cox regression model" 11 : 2093-2099, 1992

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    학술지 이력

    학술지 이력
    연월일 이력구분 이력상세 등재구분
    2022 평가 계속평가 신청대상 (등재유지)
    2017-01-01 등재 우수등재학술지 선정 (계속평가)
    2013-01-01 등재 등재학술지 유지 (등재유지) KCI등재
    2010-01-01 등재 등재학술지 유지 (등재유지) KCI등재
    2008-01-01 등재 등재학술지 유지 (등재유지) KCI등재
    2005-01-01 등재 등재학술지 선정 (등재후보2차) KCI등재
    2004-01-01 등재 등재후보 1차 PASS (등재후보1차) KCI등재후보
    2003-01-01 등재 등재후보학술지 유지 (등재후보2차) KCI등재후보
    2002-01-01 등재 등재후보 1차 PASS (등재후보1차) KCI등재후보
    2001-01-01 등재 등재후보학술지 선정 (신규평가) KCI등재후보
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    학술지 인용정보
    기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
    2016 1.18 1.18 1.07
    KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
    1.01 0.91 0.911 0.35
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