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Abstract and Applied Analysis
Volume 2013 (2013), Article ID 687151, 11 pages
Remodeling and Estimation for Sparse Partially Linear Regression Models
1Shandong University Qilu Securities Institute for Financial Studies and School of Mathematical Science, Shandong University, Jinan 250100, China
2Supercomputing Center, Shandong Computer Science Center, Jinan 250014, China
3College of Mathematics Science, Shandong Normal University, Jinan 250014, China
Received 11 October 2012; Accepted 14 December 2012
Academic Editor: Xiaodi Li
Copyright © 2013 Yunhui Zeng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- X. T. Shen, H.-C. Huang, and J. Ye, “Inference after model selection,” Journal of the American Statistical Association, vol. 99, no. 467, pp. 751–762, 2004.
- Y. Gai, L. Lin, and X. Wang, “Consistent inference for biased sub-model of high-dimensional partially linear model,” Journal of Statistical Planning and Inference, vol. 141, no. 5, pp. 1888–1898, 2011.
- Y. Zeng, L. Lin, and X. Wang, “Multi-step-adjustment consistent inference for biased sub-model of multidimensional linear regression,” Acta Mathematica Scientia, vol. 32, no. 6, pp. 1019–1031, 2012 (Chinese).
- P. Zhao and L. Xue, “Variable selection for semiparametric varying coefficient partially linear models,” Statistics & Probability Letters, vol. 79, no. 20, pp. 2148–2157, 2009.
- J. Fan and R. Li, “Variable selection via nonconcave penalized likelihood and its oracle properties,” Journal of the American Statistical Association, vol. 96, no. 456, pp. 1348–1360, 2001.
- L. Wang, G. Chen, and H. Li, “Group SCAD regression analysis for microarray time course gene expression data,” Bioinformatics, vol. 23, no. 12, pp. 1486–1494, 2007.
- L. Wang, H. Li, and J. Z. Huang, “Variable selection in nonparametric varying-coefficient models for analysis of repeated measurements,” Journal of the American Statistical Association, vol. 103, no. 484, pp. 1556–1569, 2008.
- E. F. Simas Filho and J. M. Seixas, “Nonlinear independent component analysis: theoretical review and applications,” Learning and Nonlinear Models, vol. 5, no. 2, pp. 99–120, 2007.
- J. Fan, Y. Feng, and R. Song, “Nonparametric independence screening in sparse ultra-high-dimensional additive models,” Journal of the American Statistical Association, vol. 106, no. 494, pp. 544–557, 2011.
- C. J. Stone, “Optimal global rates of convergence for nonparametric regression,” The Annals of Statistics, vol. 10, no. 4, pp. 1040–1053, 1982.
- T. W. Anderson, An Introduction to Multivariate Statistical Analysis, John Wiley & Sons, 3rd edition, 2003.
- P. Rütimann and P. Bühlmann, “High dimensional sparse covariance estimation via directed acyclic graphs,” Electronic Journal of Statistics, vol. 3, pp. 1133–1160, 2009.
- T. Cai and W. D. Liu, “Adaptive thresholding for sparse covariance matrix estimation,” Journal of the American Statistical Association, vol. 106, no. 494, pp. 672–684, 2011.
- H. Zou, “The adaptive lasso and its oracle properties,” Journal of the American Statistical Association, vol. 101, no. 476, pp. 1418–1429, 2006.
- A. Hyvärinen and E. Oja, “A fast fixed-point algorithm for independent component analysis,” Neural Computation, vol. 9, no. 7, pp. 1483–1492, 1997.
- W. Härdle, H. Liang, and J. T. Gao, Partially Linear Models, Physica, Heidelberg, Germany, 2000.