References
- Huber PJ. Robust statistics. New York: Wiley; 1981.
- Serfling RJ. Approximation theorems of mathematical statistics. New York: Wiley; 1981.
- Zhang B. M-estimation and quantile estimation in the presence of auxiliary information. J Stat Plan Inference. 1995;44:77–94.
- Hammer SM, Katzenstein DA, Hughes MD, et al. A trial comparing nucleoside monotherapy with combination therapy in HIV-infected adults with CD4 cell counts from 200 to 500 per cubic millimeter. N Engl J Med. 1996;335:1081–1089.
- Hogan JW, Laird NM. Model-based approaches to analysing incomplete longitudinal and failure time data. Stat Med. 1997;16:259–272.
- Yuan Y, Yin G. Bayesian quantile regression for longitudinal studies with nonignorable missing data. Biometrics. 2010;66:105–114.
- Little RJA, Rubin DB. Statistical analysis with missing data. 2nd ed. New York: John Wiley & Sons; 2002.
- Kim JK, Shao J. Statistical methods for handling incomplete data. London: Chapman & Hall/CRC; 2013.
- Wang S, Shao J, Kim JK. An instrument variable approach for identification and estimation with nonignorable nonresponse. Stat Sin. 2014;24:1097–1116.
- Kim JK, Yu CL. A semiparametric estimation of mean functionals with nonignorable missing data. J Amer Statist Assoc. 2011;106:157–165.
- Qin J, Leung D, Shao J. Estimation with survey data under nonignorable nonresponse or informative sampling. J Amer Statist Assoc. 2002;97:193–200.
- Shao J, Wang L. Semiparametric inverse propensity weighting for nonignorable missing data. Biometrika. 2016;103:175–187.
- Tang G, Little RJA, Raghunathan TE. Analysis of multivariate missing data with nonignorable nonresponse. Biometrika. 2003;90:747–764.
- Wang L, Deng G. Dimension-reduced empirical likelihood inference for response mean with data missing at random. J Nonparametr Stat. 2017;29:594–614.
- Wang L. Dimension reduction for kernel-assisted M-estimators with missing response at random. Ann Inst Stat Math. 2019;71:889–910.
- Ding X, Tang N. Adjusted empirical likelihood estimation of distribution function and quantile with nonignorable missing data. J Syst Sci Complexity. 2018;31:820–840.
- Wang L, Zhao P, Shao J. Dimension-reduced semiparametric estimation of distribution functions and quantiles with nonignorable nonresponse. Comput Statist Data Anal. 2021;156:107–142.
- Zhao P, Tang M, Tang N. Robust estimation of distribution functions and quantiles with non-ignorable missing data. Canadian J Statist. 2013;41:575–595.
- Fang F, Shao J. Model selection with nonignorable nonresponse. Biometrika. 2016;103:861–874.
- Miao W, Tchetgen Tchetgen EJ. Identification and inference with nonignorable missing covariate data. Stat Sin. 2018;28:2049–2067.
- Zhao J, Shao J. Semiparametric pseudo-likelihoods in generalized linear models with nonignorable missing data. J Amer Statist Assoc. 2015;110:1577–1590.
- Li KC. Sliced inverse regression for dimension reduction. J Amer Statist Assoc. 1991;86:316–327.
- Cook RD. On the interpretation of regression plots. J Amer Statist Assoc. 1994;89:177–189.
- Ma Y, Zhu L. A review on dimension reduction. Int Statist Rev. 2013;81:134–150.
- Owen AB. Empirical likelihood ratio confidence intervals for a single functional. Biometrika. 1988;75:237–249.
- Qin J, Lawless J. Empirical likelihood and general estimating equations. Ann Statist. 1994;22:300–325.
- Owen AB. Empirical likelihood ratio confidence regions. Ann Statist. 1990;18:90–120.
- Cheng PE. Nonparametric estimation of mean functionals with data missing at random. J Amer Statist Assoc. 1994;89:81–87.
- Wang D, Chen S. Empirical likelihood for estimating equations with missing values. Ann Statist. 2009;37:490–517.
- Härdle W, Müller M, Sperlich S, et al. Nonparametric and semiparametric models. Berlin: Springer-Verlag; 2004.
- Hu Z, Follmann D, Wang N. Estimation of mean response via effective balancing score. Biometrika. 2014;101:613–624.
- Zhao P, Wang L, Shao J. Sufficient dimension reduction and instrument search for data with nonignorable nonresponse. Bernoulli. 2021;27:930–945.
- Cook RD, Weisberg S. Discussion of ‘Sliced inverse regression for dimension reduction’. J Amer Statist Assoc. 1991;86:28–33.
- Xia Y, Tong H, Li W, et al. An adaptive estimation of dimension reduction space (with discussion). J R Statist Soc: Ser B (Statistical Methodology). 2002;64:363–410.
- Ding X, Wang Q. Fusion-refinement procedure for dimension reduction with missing response at random. J Amer Statist Assoc. 2011;106:1193–1207.
- Ma Y, Zhu L. A semiparametric approach to dimension reduction. J Am Stat Assoc. 2012;107:168–179.
- Rao JNK, Scott AJ. The analysis of categorical data from complex sample surveys: chi-squared tests for goodness of it and independence in twoway tables. J Amer Statist Assoc. 1981;76:221–230.
- Zhu L, Zhu L, Ferre L, et al. Sufficient dimension reduction through discretization-expectation estimation. Biometrika. 2010;97:295–304.
- Tang N, Zhao P, Zhu H. Empirical likelihood for estimating equations with nonignorably missing data. Stat Sin. 2014;24:723–747.
- Amini M, Roozbeh M. Optimal partial ridge estimation in restricted semiparametric regression models. J Multivar Anal. 2015;136:26–40.
- Roozbeh M. Optimal QR-based estimation in partially linear regression models with correlated errors using GCV criterion. Comput Statist Data Anal. 2018;117:45–61.
- Roozbeh M, Arashi M, Hamzah NA. Generalized cross-validation for simultaneous optimization of tuning parameters in ridge regression. Iranian J Sci Technol Trans A: Sci. 2020;44:473–485.