과제정보
This work is funded by the National Research Foundation of Korea (NRF) grants (2018R1D1A1B07043034, 2019R1A4A1028134) and Korea University (K2000461).
참고문헌
- David B, Royston G, Alun J, Jem JR, and Douglas BK (1997). Genetic algorithms as a method for variable selection in multiple linear regression and partial least squares regression, with applications to pyrolysis mass spectrometry, Analytica Chimica Acta, 348, 71-86. https://doi.org/10.1016/S0003-2670(97)00065-2
- Partha D and Pravin KT (1997). Demand for medical care by the elderly: A finite mixture approach, Journal of applied Econometrics, 12, 313-336. https://doi.org/10.1002/(SICI)1099-1255(199705)12:3<313::AID-JAE440>3.0.CO;2-G
- 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
- Leardi R and Gonzalez AL (1998). Genetic algorithms applied to feature selection in PLS regression: how and when to use them, Chemometrics and Intelligent Laboratory Systems, 41, 195-207. https://doi.org/10.1016/S0169-7439(98)00051-3
- Meinshausen N and Buhlmann P (2010). Stability selection, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72, 417-473. https://doi.org/10.1111/j.1467-9868.2010.00740.x
- Niazi A and Leardi R (2012). Genetic algorithms in chemometrics, Journal of Chemometrics, 26, 345-351. https://doi.org/10.1002/cem.2426
- Tibshirani R (1996). Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society Series B-methodological, 58, 267-288.
- Tibshirani R (1997). The lasso method for variable selection in the Cox model, Statistics in Medicine, 16, 385-395. https://doi.org/10.1002/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3
- Volinsky C and Raftery A(2000). Bayesian information criterion for censored survival models, Biometrics, 56, 256-262. https://doi.org/10.1111/j.0006-341X.2000.00256.x
- Wang S, Nan B, Rosset S, and Zhu J (2011). Random lasso, The Annals of Applied Statistics, 5, 468.
- Xin L and Zhu M (2012). Stochastic stepwise ensembles for variable selection, Journal of Computational and Graphical Statistics, 21, 275-294. https://doi.org/10.1080/10618600.2012.679223
- Yeh IC (2007). Modeling slump flow of concrete using second-order regressions and artificial neural networks, Cement and Concrete Composites, 29, 474-480, Available from: https://doi.org/10.10-16/j.cemconcomp.2007.02.001 https://doi.org/10.10-16/j.cemconcomp.2007.02.001
- Yuan M and Lin Y (2006). Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 68, 49-67. https://doi.org/10.1111/j.1467-9868.2005.00532.x
- Zhang CH (2010). Nearly unbiased variable selection under minimax concave penalty, The Annals of Statistics, 38, 894-942.
- Zhang CX, Zhang JS, and Kim SW (2016). PBoostGA: Pseudo-boosting genetic algorithm for variable ranking and selection, Computational Statistics, 31, 1237-1262. https://doi.org/10.1007/s00180-016-0652-8
- Zhu M and Chipman HA (2006). Darwinian evolution in parallel universes: A parallel genetic algorithm for variable selection, Technometrics, 48, 491-502. https://doi.org/10.1198/004017006000000093
- Zhu M and Fan G (2011). Variable selection by ensembles for the Cox model, Journal of Statistical Computation and Simulation, 81,1983-1992.
- Zou H and Hastie T (2005). Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67, 301-320. https://doi.org/10.1111/j.1467-9868.2005.00503.x