Partially linear models with missing response variables and error-prone covariates

Biometrika

Volumn 94, Issue 1, 2007, Pages 185-198

  Liang, Hua ^a   Wang, Suojin ^b   Carroll, Raymond J ^b  

^a UNIVERSITY OF ROCHESTER MEDICAL CENTER   (United States)

^b TEXAS A AND M UNIVERSITY   (United States)

Author keywords

Confidence region; Empirical likelihood; Estimating equation; Measurement error; Missing data; Missing not at random; Nonparametric regression; Semiparametric estimation

Indexed keywords

EID: 33947113400     PISSN: 00063444     EISSN: 14643510     Source Type: Journal    
DOI: 10.1093/biomet/asm010     Document Type: Article

References (22)

1. BICKEL, P. J., KLAASSEN, C. J., RITOV, Y. & WELLNER, J. (1993). Efficient and Adaptive Estimation for Semiparametric Models. Baltimore: Johns Hopkins University Press.
2. CARROLL, R. J., RUPPERT, D. & STEFANSKI, L. A. (1995). Measurement Error in Nonlinear Models. New York: Chapman and Hall.
3. CHEN, S. X. (1994). Empirical likelihood confidence intervals for linear regression coefficients. J. Mult. Anal. 49, 24-40.
4. CHENG, P. E. (1994). Nonparametric estimation of mean functionals with data missing at random. J. Am. Statist. Assoc. 89, 81-7.
5. 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.
6. HÄRDLE, W., LIANG, H. & GAO, J. (2000). Partially Linear Models. Heidelberg: Springer Physica-Verlag.
7. LIANG, H., HÄRDLE, W. & CARROLL, R. J. (1999). Estimation in a semiparametric partially linear errors-invariables model. Ann. Statist. 27, 1519-35.
8. LIANG, H., WANG, S., ROBINS, J. M. & CARROLL, R. J. (2004). Estimation in partially linear models with missing covariates. J. Am. Statist. Assoc. 99, 357-67.
9. LIANG, H., WU, H. L. & CARROLL, R. J. (2003). The relationship between virologic and immunologic responses in AIDS clinical research using mixed-effect varying-coefficient semiparametric models with measurement error. Biostatistics 4, 297-312.
10. LIN, X. & CARROLL, R. J. (2000). Nonparametric function estimation for clustered data when the predictor is measured without/with error. J. Am. Statist. Assoc. 95, 520-34.
11. LIN, X. & CARROLL, R. J. (2001). Semiparametric regression for clustered data using generalized estimating equations. J. Am. Statist. Assoc. 96, 1045-56.
12. NACHMAN, S. A., STANLEY, K., YOGEV, R. et al. (2000). Nucleoside analogs plus ritonavir in stable antiretroviral therapy experienced HIV infected children: a randomized controlled trial. J. Am. Med. Assoc. 283, 492-8.
13. OPSOMER, J. D. & RUPPERT, D. (1999). A root-n consistent backfitting estimator for semiparametric additive modelling. J. Comp. Graph. Statist. 8, 715-32.
14. OWEN, A. B. (2000). Empirical Likelihood. London: Chapman and Hall/CRC.
15. QIN, J. (1994). Semi-empirical likelihood ratio confidence intervals for the difference of two sample means. Ann. Inst. Statist. Math. 46, 117-26.
16. QIN, J. & LAWLESS, J. (1994). Empirical likelihood and general estimating equations. Ann. Statist. 22, 300-25.
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. SEVERINI, T. A. & STANISWALIS, J. G. (1994). Quasilikelihood estimation in semiparametric models. J. Am. Statist. Assoc. 89, 501-11.
20. SPECKMAN, P. (1988). Kernel smoothing in partial linear models. J. R. Statist. Soc. B 50, 413-36.
21. WANG, Q. H., LINTON, O. & HÄRDLE, W. (2004). Semiparametric regression analysis with missing response at random. J. Am. Statist. Assoc. 99, 334-45.
22. ZEGER, S. L. & DIGGLE, P. J. (1994). Semiparametric models for longitudinal data with application to CD4 cell numbers in HIV seroconverters. Biometrics 50, 689-99.