Dimension reduction in partly linear error-in-response models with validation data

Journal of Multivariate Analysis

Volumn 85, Issue 2, 2003, Pages 234-252

  Wang, Qihua ^a,b  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b HEILONGJIANG UNIVERSITY   (China)

Author keywords

Asymptotic normality; Dimension reduction; Partial linear model; Validation data

Indexed keywords

EID: 0037834803     PISSN: 0047259X     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0047-259X(02)00066-0     Document Type: Article

References (22)

1. J. Bound, A.B. Krueger, The extent of measurement error in longitudinal earnings data: do two wrongs make a right?, J. Labor Econometrics 9 (1989) 1-24.
2. R.J. Carroll, R.K. Knickerbocker, C.Y. Wang, Dimension reduction in a semiparametric regression model with errors in covariables, Ann. Statist. 23 (1995) 161-181.
3. R.J. Carroll, L.A. Stefanski, Approximate quasilikelihood estimation in models with surrogate predictors, J. Amer. Statist. Assoc. 85 (1990) 652-663.
4. R.J. Carroll, M.P. Wand, Semiparametric estimation in logistic measure error models, J. Roy. Statist. Soc. Ser. B 53 (1991) 652-663.
5. N. Duan, K.C. Li, Slicing regression: a link free regression method, Ann. Statist. 19 (1991) 505-530.
6. G. Duncan, D. Hill, An investigations of the extent and consequences of measurement error in labor-economics survey data, J. Labor Economics 3 (1985) 508-532.
7. W.A. Fuller, Measurement Error Models, Wiley, New York, 1987.
8. T. Hsing, R.J. Carroll, Asymptotic theory for sliced inverse regression, Ann. Statist. 20 (1992) 1040-1061.
9. L.-F. Lee, J.H. Sepanski, Estimation of linear and nonlinear error-in-variable models using validation data, J. Amer. Statist. Assoc. 90 (1995) 130-140.
10. K.C. Li, Sliced inverse regression for dimension reduction (with discussion), J. Amer. Statist. Assoc. 86 (1991) 337-342.
11. M.S. Pepe, Inference using surrogate outcome data and a validation sample, Biometrika 79 (1992) 355-365.
12. M.S. Pepe, T.R. Fleming, A general nonparametric method for dealing with errors in missing or surrogate covariable data, J. Amer. Statist. Assoc. 86 (1991) 108-113.
13. B.L.S.P. Rao, Nonparametric Functional Estimation, Academic Press, Inc., London, 1983.
14. J.H. Sepanski, R.J. Carroll, Semiparametric quasilikelihood and variance function estimation in measurement error models, J. Econometrics 58 (1993) 223-256.
15. J.H. Sepanski, R. Kinickerbocker, R.J. Carroll, A semiparametric correction for attenuation, J. Amer. Statist. Assoc. 89 (1994) 1366-1373.
16. J.H. Sepanski, L.F. Lee, Semiparametric estimation of nonlinear error-in-variables models with validation study, J. Nonparam. Statistics 4 (1995) 365-394.
17. P. Speckman, Kernel smoothing in partial linear models, J. Roy. Statist. Soc. Ser. B 50 (1988) 413-436.
18. L.A. Stefanski, R.J. Carroll, Conditional scores and optimal scores for generalized linear measurement error models, Biometrika 74 (1987) 703-716.
19. C.J. Stone, Optimal rates of convergence for nonparametric estimators, Ann. Statist. 8 (1980) 1348-1360.
20. Q.H. Wang, Estimation of partial linear error-in-variables model with validation data, J. Multivariate Anal. 69 (1999) 30-64.
21. J. Wittes, E. Lakatos, J. Probstfied, Surrogate endpoints in clinical trails: cardiovascular diseases, Statist. Med. 8 (1989) 415-425.
22. L.X. Zhu, K.T. Fang, Asymptotics for kernel estimator of sliced inverse regression, Ann. Statist. 24 (1996) 1053-1068.