Tuning parameter selectors for the smoothly clipped absolute deviation method

Biometrika

Volumn 94, Issue 3, 2007, Pages 553-568

  Wang, Hansheng ^a   Li, Runze ^b   Tsai, Chih Ling ^c  

^a Peking University   (China)

^b PENNSYLVANIA STATE UNIVERSITY   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

AIC; BIC; Generalized crossvalidation; Least absolute shrinkage and selection operator; Smoothly clipped absolute deviation

Indexed keywords

EID: 34548536572     PISSN: 00063444     EISSN: 14643510     Source Type: Journal    
DOI: 10.1093/biomet/asm053     Document Type: Article

References (26)

1. AKAIKE, H. (1973). Information theory and an extension of the maximum likelihood principle. In Proceedings of the 2nd International Symposium Information Theory, Ed. B. N. Petrov and F. Csaki, pp. 267-81. Budapest: Akademia Kiado.
2. BREIMAN, L. (1996). Heuristics of instability and stabilization in model selection. Ann. Statist. 24, 2350-3.
3. BUNEA, F. (2004). Consistent covariate selection and post model selection inference in semiparametric regression. Ann. Statist. 32, 898-927.
4. BUNEA, F. & WEGKAMP, M. (2004). Two-stage model selection procedures in partially linear regression. Can. J. Statist. 32, 105-118.
5. CRAVEN, P. & WAHBA, G. (1979). Smoothing noisy data with spline function: Estimating the correct degree of smoothing by the method of generalized cross validation. Numer. Math. 31, 337-403.
6. ENGLE, R. F., GRANGER, C. W. J., RICE, J. & WEISS, A. (1986). Semiparametric estimates of the relation between weather and electricity sales. J. Am. Statist. Assoc. 81, 310-20.
7. FAN, J. & GIJBELS, I. (1996). Local Polynomial Modelling and Its Applications. New York: Chapman and Hall.
8. FAN, J., HÄRDLE, W. & MAMMEN, E. (1998). Direct estimation of low-dimensional components in additive models. Ann. Statist. 26, 943-71.
9. FAN, J. & HUANG, T. (2005). Profile likelihood inference on semiparametric varying-coefficient partially linear models. Bernoulli 11, 1031-57.
10. FAN, J. & LI, R. (2001). Variable selection via nonconcave penalised likelihood and its oracle properties. J. Am. Statist. Assoc. 96, 1348-60.
11. FAN, J. & LI, R. (2004). New estimation and model selection procedures for semiparametric modeling in longitudinal data analysis. J. Am. Statist. Assoc. 99, 710-23.
12. HÄRDLE, W., LIANG, H. & GAO, J. (2000). Partially Linear Models. Heidelberg: Springer Physica-Verlag.
13. HECKMAN, N. E. (1986). Spline smoothing in partly linear models. J. R. Statist. Soc. B 48, 244-8.
14. HUANG, J. & YANG, L. (2004). Identification of non-linear additive autoregressive models. J. R. Statist. Soc. B 66, 463-77.
15. MACK, Y. P. & SILVERMAN, B. W. (1982). Weak and strong uniform consistency of kernel regression estimates. Z. Wahr. Verw. Geb. 61, 405-15.
16. MCQUARRIE, D. R. & TSAI, C. L. (1998). Regression and Time Series Model Selection. Singapore: World Scientific.
17. ROBINSON, P. M. (1988). Root-n-consistent semiparametric regression. Econometrica 56, 931-54.
18. RUPPERT, D., SHEATHER, S. J. & WAND, M. P. (1995). An effective bandwidth selector for local least squares regression. J. Am. Statist. Assoc. 90, 1257-70.
19. SCHWARZ, G. (1978). Estimating the dimension of a model. Ann. Statist. 6, 461-4.
20. SHAO, J. (1997). An asymptotic theory for linear model selection. Statist. Sinica 7, 221-64.
21. SHI, P. & TSAI, C. L. (2002). Regression model selection-a residual likelihood approach. J. R. Statist. Soc. B 64, 237-52.
22. SHI, P. & TSAI, C. L. (2004). A joint regression variable and autoregressive order selection criterion. J. Time Ser. Anal. 25, 923-41.
23. SPECKMAN, P. (1988). Kernel smoothing in partially linear models. J. R. Statist. Soc. B 50, 413-36.
24. TIBSHIRANI, R. J. (1996). Regression shrinkage and selection via the lasso. J. R. Statist. Soc. B 58, 267-88.
25. YANG, Y. (2005). Can the strengths of AIC and BIC be shared? A conflict between model identification and regression estimation. Biometrika 92, 973-50.
26. YATCHEW, A. (1997). An elementary estimator for the partially linear model. Economet. Lett. 57, 135-43.