Estimating linear functionals in nonlinear regression with responses missing at random

Annals of Statistics

Volumn 37, Issue 5 A, 2009, Pages 2245-2277

  Müller, Ursula U ^a  

^a TEXAS A AND M UNIVERSITY   (United States)

Author keywords

Confidence interval; Empirical likelihood; Gradient; Influence function; Semiparametric regression; Weighted empirical estimator

Indexed keywords

EID: 69049088841     PISSN: 00905364     EISSN: None     Source Type: Journal    
DOI: 10.1214/08-AOS642     Document Type: Article

References (10)

1. Bickel, P. J. (1982). On adaptive estimation. Ann. Statist. 10 647-671. MR0663424
2. Bickel, P. J., Klaassen, C.A. J., Ritov, Y. and Wellner, J. A. (1998). Efficient and Adaptive Estimation for Semiparametric Models. Springer, New York. MR1623559
3. Chen, J., Fan, J., Li, K.H. and Zhou, H. (2006). Local quasi-likelihood estimation with data missing at random. Statist. Sinica 16 1071-1100. MR2327481
4. Chen, S.X. and Wang, D. (2009). Empirical likelihood for estimating equations with missing values. Ann. Statist. 37 490-517.
5. Chen, X., Hong, H. and Tarozzi, A. (2008). Semiparametric efficiency in GMM models with auxiliary data. Ann. Statist. 36 808-843. MR2396816
6. Cheng, P. E. (1994). Nonparametric estimation of mean functionals with data missing at random. J. Amer. Statist. Assoc. 89 81-87.
7. Forrester, J., Hooper, W., Peng, H. and Schick, A. (2003). On the construction of efficient estimators in semiparametric models. Statist. Decisions 21 109-138. MR2000666
8. Gelman, A., Carlin, J. B., Stern, H. S. and Rubin, D. B. (1995). Bayesian Data Analysis. Chapman & Hall, London. MR1385925
9. Koul, H.L. and Susarla, V. (1983). Adaptive estimation in linear regression. Statist. Decisions 1 379-400. MR0736109
10. Liang, H., Wang, S. and Carroll, R. J. (2007). Partially linear models with missing response variables and error-prone covariates. Biometrika 94 185-198. MR2307903