References
- Wu L, Yang YH, Liu HZ. Nonnegative-lasso and application in index tracking. Comput Statist Data Anal. 2014;70:116–126. doi: 10.1016/j.csda.2013.08.012
- Wu L, Yang YH. Nonnegative elastic net and application in index tracking. Appl Math Comput. 2014;227:541–552.
- Yang YH, Wu L. Nonnegative adaptive lasso for ultra-high dimensional regression models and a two-stage method applied in financial modeling. J Statist Plann Inference. 2016;174:52–67. doi: 10.1016/j.jspi.2016.01.011
- Li N, Yang H. Nonnegative estimation and variable selection under minimax concave penalty for sparse high-dimensional linear regression models. Statist Pap. 2019; in press.
- Koenker R. Quantile regression. Cambridge: Cambridge University Press; 2005.
- Wang H, Li G, Jiang G. Robust regression shrinkage and consistent variable selection through the LAD-Lasso. J Business Economic Statist. 2007;25:347–355. doi: 10.1198/073500106000000251
- Wu Y, Liu Y. Variable selection in quantile regression. Stat Sinica. 2009;19:801–817.
- Zou H, Yuan M. Composite quantile regression and the oracle model selection theory. Ann Stat. 2008;36:1108–1126. doi: 10.1214/07-AOS507
- Guo J, Tian M, Zhu K. New efficient and robust estimation in varying-coefficient models with heteroscedasticity. Statist Sinica. 2012;22:1075–1101.
- Zhao KF, Lian H. A note on the efficiency of composite quantile regression. J Stat Comput Simul. 2016;7:1334–1341. doi: 10.1080/00949655.2015.1062096
- Meinshausen N. Sign-constrained least squares estimation for high-dimensional regression. Electron J Stat. 2013;7:1607–1631. doi: 10.1214/13-EJS818
- Slawski M, Hein M. Non-negative least squares for high-dimensional linear models: consistency and sparse recovery without regularization. Electron J Stat. 2013;7:3004–3056. doi: 10.1214/13-EJS868
- Yuan M, Lin Y. On the non-negative garrotte estimator. J R Statist Soc Ser B. 2007;69:143–161. doi: 10.1111/j.1467-9868.2007.00581.x
- Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Statist Soc Ser B. 2005;67:301–320. doi: 10.1111/j.1467-9868.2005.00503.x
- Zou H. The adaptive lasso and its oracle properties. J Amer Statist Assoc. 2006;101:1418–1429. doi: 10.1198/016214506000000735
- Fan J, Li R. Variable selection via nonconcave penalized likelihood and its oracle properties. J Amer Statist Assoc. 2001;96:1348–1360. doi: 10.1198/016214501753382273
- Zhang CH. Nearly unbiased variable selection under minimax concave penalty. Ann Stat. 2010;38:894–942. doi: 10.1214/09-AOS729
- Tibshirani R. Regression shrinkage and selection via the LASSO. J R Statist Soc, Ser B. 1996;58:267–288.
- Zou H, Li R. One-step sparse estimates in nonconcave penalized likelihood models (with discussion). Ann Stat. 2008;36:1509–1533. doi: 10.1214/009053607000000802
- Breiman L. Better subset regression using the Nonnegative Garrote. Technometrics. 1995;37:373–384. doi: 10.1080/00401706.1995.10484371
- Akaike H. Maximum Likelihood Identification of Gaussian Autoregressive Moving Average Models. Biometrika. 1973;60:255–265. doi: 10.1093/biomet/60.2.255
- Schwarz G. Estimating the dimension of a model. Ann Stat. 1978;6:461–464. doi: 10.1214/aos/1176344136
- Mallows CL. Some comments on Cp. Technometrics. 1973;15:661–675.
- Connor G, Leland H. Cash management for index tracking. Financ. Anal. J. 1995;51:75–80. doi: 10.2469/faj.v51.n6.1952
- Franks E. Targeting excess-of-benchmark returns. J Portfolio Manage. 1992;18:6–12. doi: 10.3905/jpm.1992.409419
- Jacobs B, Levy K. Residual risk: how much is too much. J Portfolio Manage. 1996;22:10–15. doi: 10.3905/jpm.1996.10
- Jobst N, Horniman M, Lucas C, et al. Computational aspects of alternative portfolio selection models in the presence of discrete asset choice constraints. Quant Finance. 2001;1:1–13. doi: 10.1088/1469-7688/1/5/301
- Roll R. A mean/variance analysis of tracking error. J Portfolio Manage. 1992;18:13–22. doi: 10.3905/jpm.1992.701922
- Toy W, Zurack M. Tracking the Euro-Pac index. J Portfolio Manage. 1989;15:55–58. doi: 10.3905/jpm.1989.409186
- Choi YG, Lim J, Choi S. High-dimensional Markowitz portfolio optimization problem: empirical comparison of covariance matrix estimators. J Stat Comput Simul. 2019;89:1278–1300. doi: 10.1080/00949655.2019.1577855