과제정보
이 논문은 부경대학교 자율창의학술연구비(2019년)에 의하여 연구되었음.
참고문헌
- Antoniadis A, Gijbels I, Lambert-Lacroix S, and Poggi J (2016). Joint estimation and variable selection for mean and dispersion in proper dispersion models, Electronic Journal of Statistics, 10, 1630-1676. https://doi.org/10.1214/16-EJS1152
- Charalambous C, Pan J, and Tranmer M (2015). Variable selection in joint modelling of the mean and variance for hierarchical data, Statistical Modelling, 15, 24-50. https://doi.org/10.1177/1471082X13520424
- Cox DR (1972). Regression models and life-tables. Journal of the Royal Statistical Society-Series B, 34, 187-220.
- Fan J and Li R (2001). Variable selection via nonconcave penalized likelihood and its oracle properties, Journal of the American Statistical Association, 96, 1348-1360. https://doi.org/10.1198/016214501753382273
- Fan J and Li R (2002). Variable selection for Cox's proportional hazards model and frailty model, The Annals of Statistics, 30, 74-99. https://doi.org/10.1214/aos/1015362185
- Ha ID, Lee Y, and Song JK (2002). Hierarchical likelihood approach for mixed linear models with censored data, Lifetime Data Analysis, 8, 163-176. https://doi.org/10.1023/A:1014839723865
- Ha ID, Pan J, Oh S, and Lee Y (2014). Variable selection in general frailty models using penalized h-likelihood, Journal of Computational and Graphical Statistics, 23, 1044--1060 https://doi.org/10.1080/10618600.2013.842489
- Ha ID, Jeong JH, and Lee Y (2017). Statistical Modelling of Survival Data with Random Effects: H-Likelihood Approach, Springer, Singapore.
- Hutton JL and Monaghan PF (2002). Choice of parametric accelerated life and proportional hazard models for survival data: asymptotic results, Lifetime Data Analysis, 8, 375-393. https://doi.org/10.1023/A:1020570922072
- Klein JP and Moeschberger ML (2003). Survival Analysis : Techniques for Censored and Truncated Data(2nd ed), Springer, New York.
- Lawless JF (1982). Statistical Models and Methods for Lifetime Data, Wiley, New York.
- Lee Y and Oh H (2014). A new sparse variable selection via random-effect model, Journal of Multivariate Analysis, 125, 89-99. https://doi.org/10.1016/j.jmva.2013.11.016
- MacKenzie G (1996). Regression models for survival data: the generalized time-dependent logistic family, The Statistician, 45, 21-34. https://doi.org/10.2307/2348408
- Nelder JA and Lee Y (1998). Joint modeling of mean and dispersion, Technometrics, 40, 168-171. https://doi.org/10.1080/00401706.1998.10485225
- Nedler JA and Wedderburn RWM (1972). Generalized linear models, Journal of the Royal Statistical Society A, 135, 370-384. https://doi.org/10.2307/2344614
- Park E and Ha ID (2018). Penalized variable selection for accelerated failure time models, Communications for Statistical Applications and Methods, 25, 591-604. https://doi.org/10.29220/CSAM.2018.25.6.591
- Tibshirani R (1996). Regression shrinkage and selection via the Lasso, Journal of the Royal Statistical Society B, 58, 267-288.
- Wang H, Li R, and Tsai CL (2007). Tuning parameter selectors for the smoothly clipped absolute deviation method, Biometrika, 94, 553--568. https://doi.org/10.1093/biomet/asm053
- Wang X and Song L (2011). Adaptive lasso variable selection for the accelerated failure models, Communications in Statistics - Theory and Methods, 40, 4372-4386. https://doi.org/10.1080/03610926.2010.513785
- Wu L and Li H (2012). Variable selection for joint mean and dispersion models of the inverse Gaussian distribution, Metrika, 75, 795-808. https://doi.org/10.1007/s00184-011-0352-x
- Zhou M (2005). Empirical likelihood analysis of the rank estimator for the censored accelerated failure time model, Biometrika, 92, 492-498. https://doi.org/10.1093/biomet/92.2.492
- Zou H (2006). The adaptive Lasso and its oracle properties. Journal of American Statistical Association, 101, 1418-1429. https://doi.org/10.1198/016214506000000735