Group variable selection with oracle property by weight-fused adaptive elastic net model for strongly correlated data

Communications in Statistics: Simulation and Computation

Volumn 43, Issue 10, 2014, Pages 2468-2481

  Fu, Guang Hui ^a   Zhang, Wen Ming ^b   Dai, Lin ^a   Fu, Ying Zi ^a  

^a KUNMING UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b Information Center of Kunming Changshui International Airport   (China)

Author keywords

Adaptive elastic net; Group variable selection; LASSO; Oracle property; Weight fused LASSO

Indexed keywords

COMMUNICATION; COMPUTER SIMULATION;

ELASTIC NET; LASSO; ORACLE PROPERTIES; VARIABLE SELECTION; WEIGHT-FUSED LASSO;

STATISTICS;

EID: 84902656830     PISSN: 03610918     EISSN: 15324141     Source Type: Journal    
DOI: 10.1080/03610918.2012.752841     Document Type: Article

References (18)

1. Daye, Z. J., Jeng, X. J. (2009). Shrinkage and model selection with correlated variables via weighted fusion. Computational Statistics & Data Analysis 53 (4): 1284-1298.
2. Despagne, F., Massart, D., Chabot, P. (2000). Development of a robust calibration model for nonlinear in-line process data. Analytical Chemistry 72 (7): 1657-1665.
3. Efron, B., Hastie, T., Johnstone, I., Tibshirani, R. (2004). Least angle regression. The Annals of statistics 32 (2): 407-499.
4. Fan, J., Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association 96 (456): 1348-1360.
5. Fan, J., Lv, J. (2008). Sure independence screening for ultrahigh dimensional feature space. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 70 (5): 849-911.
6. Fu, G., Xu, Q. (2012). Grouping variable selection by weight fused elastic net for multi-collinear data. Communications in Statistics-Simulation and Computation 41 (2): 205-221.
7. Harrell, F., Harrell, F. (2001). Regression Modeling Strategies. New York: Springer.
8. Hoerl, A., Kennard, R. (1970). Ridge regression: Applications to nonorthogonal problems. Technometrics 12 (1): 69-82.
9. Hoerl, A., Kennard, R. (2000). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 42 (1): 80-86.
10. Huang, X., Xu, Q., Liang, Y. (2012). PLS regression based on sure independence screening for multivariate calibration. Analytical Methods 4: 2815-2821.
11. Næs, T., Martens, H. (1988). Principal component regression in NIR analysis: Viewpoints, background details and selection of components. Journal of Chemometrics 2 (2): 155-167.
12. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological) 58 (1): 267-288.
13. Tibshirani, R., Saunders, M., Rosset, S., Zhu, J., Knight, K. (2005). Sparsity and smoothness via the fused lasso. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67 (1): 91-108.
14. Wold, S., Sjostrom, M., Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 58 (2): 109-130.
15. Xu, Q., Liang, Y., Shen, H. (2001). Generalized PLS regression. Journal of Chemometrics 15 (3): 135-148.
16. Zou, H. (2006). The adaptive lasso and its oracle properties. Journal of the American Statistical Association 101 (476): 1418-1429.
17. Zou, H., Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67 (2): 301-320.
18. Zou, H., Zhang, H. (2009). On the adaptive elastic-net with a diverging number of parameters. Annals of Statistics 37 (4): 1733-1751.