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    H-likelihood approach for variable selection in gamma frailty models

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    https://www.riss.kr/link?id=A104152148

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    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    Recently, variable selection methods using penalized likelihood with a shrink penalty function have been widely studied in various statistical models including generalized linear models and survival models. In particular, they select important variables and estimate coefficients of covariates simultaneously. In this paper, we develop a penalized h-likelihood method for variable selection in gamma frailty models. For this we use the smoothly clipped absolute deviation (SCAD) penalty function, which satisfies a good property in variable selection. The proposed method is illustrated using simulation study and a practical data set.
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    Recently, variable selection methods using penalized likelihood with a shrink penalty function have been widely studied in various statistical models including generalized linear models and survival models. In particular, they select important variabl...

    Recently, variable selection methods using penalized likelihood with a shrink penalty function have been widely studied in various statistical models including generalized linear models and survival models. In particular, they select important variables and estimate coefficients of covariates simultaneously. In this paper, we develop a penalized h-likelihood method for variable selection in gamma frailty models. For this we use the smoothly clipped absolute deviation (SCAD) penalty function, which satisfies a good property in variable selection. The proposed method is illustrated using simulation study and a practical data set.

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    참고문헌 (Reference)

    1 김길훈, "손상으로 인한 사망자의 지역별 차이에 대한 HGLM을 이용한 연구" 한국데이터정보과학회 22 (22): 137-148, 2011

    2 Fan, J., "Variable selection via nonconcave penalized likelihood and its oracle properties" 96 : 1348-1360, 2001

    3 Lu, W., "Variable selection for proportional odds model" 26 : 3771-3781, 2007

    4 Fan, J., "Variable selection for Cox's proportional hazards model and frailty model" 30 : 74-99, 2002

    5 Duchateau, L., "The frailty model" Springer-Verlag 2008

    6 Zou, H., "The adaptive Lasso and its oracle properties" 101 : 1418-1429, 2006

    7 Andersen, P. K., "Testing for centre effects in multi-centre survival studies: A monte carlo comparison of xed and random effects tests" 18 : 1489-1500, 1999

    8 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

    9 Tibshirani, R., "Regression shrinkage and selection via the Lasso" 58 : 267-288, 1996

    10 하일도, "Inference for heterogeneity of treatment effect in multi-center clinical trial" 한국데이터정보과학회 22 (22): 605-612, 2011

    1 김길훈, "손상으로 인한 사망자의 지역별 차이에 대한 HGLM을 이용한 연구" 한국데이터정보과학회 22 (22): 137-148, 2011

    2 Fan, J., "Variable selection via nonconcave penalized likelihood and its oracle properties" 96 : 1348-1360, 2001

    3 Lu, W., "Variable selection for proportional odds model" 26 : 3771-3781, 2007

    4 Fan, J., "Variable selection for Cox's proportional hazards model and frailty model" 30 : 74-99, 2002

    5 Duchateau, L., "The frailty model" Springer-Verlag 2008

    6 Zou, H., "The adaptive Lasso and its oracle properties" 101 : 1418-1429, 2006

    7 Andersen, P. K., "Testing for centre effects in multi-centre survival studies: A monte carlo comparison of xed and random effects tests" 18 : 1489-1500, 1999

    8 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

    9 Tibshirani, R., "Regression shrinkage and selection via the Lasso" 58 : 267-288, 1996

    10 하일도, "Inference for heterogeneity of treatment effect in multi-center clinical trial" 한국데이터정보과학회 22 (22): 605-612, 2011

    11 Ha, I. D., "Hierarchical likelihood approach for frailty models" 88 : 233-243, 2001

    12 Lee, Y., "Hierarchical generalized linear models (with discussion)" 58 : 619-678, 1996

    13 Lee, Y., "Hierarchical generalised linear models: A synthesis of generalised linear models, random-effect models and structured dispersions" 88 : 987-1006, 2001

    14 Lee, Y., "Generalised linear models with random effects: Unified analysis via h-likelihood" Chapman and Hall 2006

    15 Ha, I. D., "Frailty modelling for survival data from multi-centre clinical trials" WILEY-BLACKWELL 30/17 : 2144-2159, 2011

    16 홍연웅, "Failure rate of a bivariate exponential distribution" 한국데이터정보과학회 21 (21): 173-177, 2010

    17 Ha, I. D., "Estimating frailty models via Poisson hierarchical generalized linear models" 12 : 663-681, 2003

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

    19 Breslow, N. E., "Discussion of professor Cox's paper" 34 : 216-217, 1972

    20 Fleming, T. R., "Counting processes and survival analysis" Wiley 1991

    21 Ha, I. D., "Bias Reduction of Likelihood Estimators in Semiparametric Frailty Models" Wiley-Blackwell 37 : 307-320, 2010

    22 Hougaard, P., "Analysis of multivariate survival data" Springer-Verlag 2000

    23 Nielsen, G. G., "A counting process approach to maximum likelihood estimation in frailty models" 19 : 25-44, 1992

    24 하일도, "A correction of SE from penalized partial likelihood in frailty models" 한국데이터정보과학회 20 (20): 895-903, 2009

    25 하일도, "A HGLM framework for Meta-Analysis of Clinical Trials with Binary Outcomes" 한국데이터정보과학회 19 (19): 1429-1440, 2008

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