References
- Hampel FR, Ronchetti EM, Rousseeuw PJ, et al. Robust statistics: the approach based on influence functions. New York: Wiley; 1986.
- Rousseeuw PJ, Leroy AM. Robust regression and outlier detection. Wiley series in probability and mathematical statistics: applied probability and statistics. New York: Wiley; 1987.
- Weisberg S. Applied linear regression. 2nd edn. New York: Wiley; 1985.
- She Y, Owen AB. Outlier detection using nonconvex penalized regression. J Amer Stat Assoc. 2011;106:626–639. doi:10.1198/jasa.2011.tm10390
- Lee Y, MacEachern SN, Jung Y. Regularization of case-specific parameters for robustness and efficiency. Stat Sci. 2012;27:350–372. doi:10.1214/11-STS377
- Yu C, Chen K, Yao W. Outlier detection and robust mixture modeling using nonconvex penalized likelihood. J Stat Plann Inference. 2015;164:27–38. doi:10.1016/j.jspi.2015.03.003
- Bickel PJ, Li B. Regularization in statistics (with discussion). Test. 2006;15:271–344. doi:10.1007/BF02607055
- Tibshirani RJ. Regression shrinkage and selection via the Lasso. J R Stat Soc Ser B. 1996;58:267–288.
- Fan J, Li R. Variable selection via nonconcave penalized likelihood and its oracle properties. J Amer Stat Assoc. 2001;96:1348–1360. doi:10.1198/016214501753382273
- Zou H. The adaptive lasso and its oracle properties. J Amer Stat Assoc. 2006;101:1418–1429. doi:10.1198/016214506000000735
- Zou H, Yuan M. Composite quantile regression and the oracle model selection theory. Ann Stat.
- Wu Y, Liu Y. Variable selection in quantile regression. Stat Sin. 2009;19:801–817.
- Greenshtein E, Ritov Y. Persistence in high-dimensional predictor selection and the virtue of overparametrization. Bernoulli. 2004;10:971–988. doi:10.3150/bj/1106314846
- Meinshausen N, Buhlmann P. High dimensional graphs and variable selection with the LASSO. Ann Stat. 2006;34:1436–1462. doi:10.1214/009053606000000281
- Zhao P, Yu B. On model selection consistency of lasso. J Mach Learn Res. 2006;7:2541–2563.
- Dette H, Munk A. Testing heteroscedasticity in nonparametric regression. J R Stat Soc Ser B. 1998;60:693–708. doi:10.1111/1467-9868.00149
- Weisberg S. Applied linear regression. New York: Wiley; 1980.
- Breusch TS, Pagan AR. A simple test for heteroscedasticity and random coefficient variation. Econometrica. 1979;47:1287–1294. doi:10.2307/1911963
- White H. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica. 1980;48:817–838. doi:10.2307/1912934
- Harvey AC. Estimating regression models with multiplicative heteroskedasticity. Econometrica. 1976;44:461–465. doi:10.2307/1913974
- Glesjer H. A new test for heteroskedasticity. J Amer Stat Assoc. 1969;64:316–323. doi:10.1080/01621459.1969.10500976
- Wang H, Li R, Tsai CL. Tuning parameter selectors for the smoothly clipped absolute deviation method. Biometrika. 2007;94:553–568. doi:10.1093/biomet/asm053
- Zhu L-P, Zhu L-X. Nonconcave penalized inverse regression in single-index models with high dimensional predictors. J Multivar Anal. 2009;100:862–875. doi:10.1016/j.jmva.2008.09.003
- Cui X. Statistical analysis of two types of complex data and its associated model [Ph.D. Thesis]. Jinan: Shandong University; 2008.