[Bic82 [BKRW98

[Car82 [Che94




[KS83 [Mat81 [MS87

Bickel, P.J.: On adaptive estimation. Ann. Statist. 10, 647-671 (1982)

Bickel, P.J., Klaassen, C.A.J., Ritov, Y., Wellner, J.A.: Efficient and Adaptive Estimation for Semiparametric Models. Springer, New York (1998)

Carroll, R.J.: Adapting for heteroscedasticity in linear models. Ann. Statist. 10, 1224-1233 (1982)

Cheng, P.E.: Nonparametric estimation of mean functionals with data missing at random. J. Amer. Statist. Assoc. 89, 81-87 (1994)

Cheng, P.E., Chu, C.K.: Kernel estimation of distribution functions and quantiles with missing data. Statist. Sinica 6, 63-78 (1996)

Hirano, K., Imbens, G.W., Ridder, G.: Efficient estimation of average treatment effects using the estimated propensity score. Econometrica 71, 1161-1189 (2003).

Ibragimov, I.A., Has'minskUi , R.Z.: Statistical Estimation. Asymptotic Theory. Applications of Mathematics 16, Springer, New York (1981)

Koul, H.L., Susarla, V.: Adaptive estimation in linear regression. Statist. Decisions 1, 379-400 (1983)

Matloff, N.S.: Use of regression functions for improved estimation of means. Biometrika 68, 685-689 (1981) Müller, H.-G., Stadtmüller, U.: Estimation of heteroscedasticity in regression analysis. Ann. Statist. 15, 610-625 (1987)

[MSW04] Müller, U.U., Schick, A., Wefelmeyer, W.: Estimating function-als of the error distribution in parametric and nonparametric regression. J. Nonparametr. Statist. 16, 525-548 (2004) [NEW04] Nan, B., Emond, M., Wellner, J.A.: Information bounds for Cox regression models with missing data. Ann. Statist. 32, 723-753 (2004)

[RbRt95] Robins, J.M., Rotnitzky, A.: Semiparametric efficiency in multivariate regression models with missing data. J. Amer. Statist. Assoc. 90, 122-129(1995) [RRZ94] Robins, J.M., Rotnitzky, A., Zhao, L.P.: Estimation of regression coefficients when some regressors are not always observed. J. Amer. Statist. Assoc. 89, 846-866 (1994) [Rob87] Robinson, P.M.: Asymptotically efficient estimation in the presence of heteroskedasticity of unknown form. Econometrica 55, 875-891 (1987)

[RtRb95] Rotnitzky, A., Robins, J.M.: Semi-parametric estimation of models for means and covariances in the presence of missing data. Scand. J. Statist. 22, 323-333 (1995) [Sch87] Schick, A.: A note on the construction of asymptotically linear estimators. J. Statist. Plann. Inference 16, 89-105 (1987) [Sch93] Schick, A.: On efficient estimation in regression models. Ann. Statist. 21, 1486-1521 (1993). Correction and addendum: 23, 1862-1863 (1995)

[SR01] Schisterman, E., Rotnitzky, A.: Estimation of the mean of a ^-sample U-statistic with missing outcomes and auxiliaries. Biometrika 88, 713-725 (2001) [Tam78] Tamhane, A.C.: Inference based on regression estimator in double sampling. Biometrika 65, 419-427 (1978) [WHL04] Wang, Q., Härdle, W., Linton, O.: Semiparametric regression analysis under imputation for missing response data. J. Amer. Statist. Assoc. 99, 334-345 (2004) [WR01] Wang, Q., Rao, J.N.K.: Empirical likelihood for linear regression models under imputation for missing responses. Canad. J. Statist. 29, 597-608 (2001) [WR02] Wang, Q., Rao, J.N.K.: Empirical likelihood-based inference under imputation for missing response data. Ann. Statist. 30, 896924 (2002)

[YN03] Yu, M., Nan, B.: Semiparametric regression models with missing data: the mathematics in the work of Robins et al. Technical Report, Department of Biostatistics, University of Michigan (2003).^bnan/

Was this article helpful?

0 0

Post a comment