Linear regression after spline transformation

Biometrika

Volumn 84, Issue 2, 1997, Pages 474-481

  Xuming, H E ^a   Shen, Lui ^b  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^b CENTER FOR DRUG EVALUATION AND RESEARCH   (United States)

Author keywords

B spline; Box cox transformation; Canonical analysis; Generalised linear model; Model selection; Slicing regression

Indexed keywords

EID: 0001624789     PISSN: 00063444     EISSN: None     Source Type: Journal    
DOI: 10.1093/biomet/84.2.474     Document Type: Article

References (10)

1. BOX, G. E. P. & Cox, D. R. (1964). An analysis of transformations (with Discussion). J. R. Statist. Soc. B 26, 211-52.
2. CHEN, C. H. & Li, K.-C. (1997). Can SIR be as popular as multiple linear regression? Statist. Sinica. To appear.
3. DUAN, N. & Li, K.-C. (1991). Slicing regression: A link-free regression method. Ann. Statist. 19, 505-30.
4. HE, X. & SHI, P. (1994). Convergence rate of B-spline estimators of nonparametric conditional quantité functions. J. Nonparam. Statist. 3, 299-308.
5. HINKLEY, D. V. & RUNGER, G. (1984). The analysis of transformed data (with Discussion). J. Am. Statist. Assoc. 78, 302-9.
6. HUBER, P. J. (1973). Robust regression: Asymptotics, conjectures and Monte Carlo. Ann. Statist. 1, 799-821.
7. LI, K.-C. & DUAN, N. (1989). Regression analysis under link violation. Ann. Statist. 17, 1009-52.
8. LINHART, H. & ZUCCHINI, W. (1986). Model Selection. New York: John Wiley.
9. SCHUMAKER, L. L. (1981). Spline Functions. New York: Wiley.
10. SCHWARZ, G. (1978). Estimating the dimension of a model. Ann. Statist. 6, 461-4.