Statistical inference for nonignorable missing-data problems: a selective review

References

• Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. Breakthroughs in statistics. New York: Springer.
   Google Scholar
• Bentler, P. M., & Wu, E. J. C. (2002). EQS6.0 for windows user guide. Encino, CA: Multivariate Software.
   Google Scholar
• Berger, J. O. (1994). An overview of robust Bayesian analysis. Test, 3, 5–124. doi: 10.1007/BF02562676
   Google Scholar
• Caner, M., & Zhang, H. H. (2014). Adaptive elastic net for generalized methods of moments. Journal of Business & Economic Statistics, 32, 30–47. doi: 10.1080/07350015.2013.836104
   PubMed Web of Science ®Google Scholar
• Chang, T., & Kott, P. S. (2008). Using calibration weighting to adjust for nonresponse under a plausible model. Biometrika, 95, 555–571. doi: 10.1093/biomet/asn022
   Web of Science ®Google Scholar
• Chen, S. X., & Kim, J. K. (2017). Semiparametric fractional imputation using empirical likelihood in survey sampling. Statistical Theory and Related Fields, 1, 69–81. doi: 10.1080/24754269.2017.1328244
   PubMedGoogle Scholar
• Chen, J., Sitter, R. R., & Wu, C. (2002). Using empirical likelihood methods to obtain range restricted weights in regression estimators for surveys. Biometrika, 89, 230–237. doi: 10.1093/biomet/89.1.230
   Web of Science ®Google Scholar
• Cheng, P. E. (1994). Nonparametric estimation of mean functionals with data missing at random. Journal of the American Statistical Association, 89, 81–87. doi: 10.1080/01621459.1994.10476448
   Web of Science ®Google Scholar
• Cook, R. D. (1977). Detection of influential observation in linear regression. Technometrics, 19, 15–18.
   Web of Science ®Google Scholar
• Cook, R. D. (1986). Assessment of local influence. Journal of the Royal Statistical Society Series B, 48, 133–169.
   Google Scholar
• Cook, R. D., & Weisberg, S. (1982). Residuals and influence in regression. New York: Chapman and Hall.
   Google Scholar
• Cui, H. J., Li, R. Z., & Zhong, W. (2015). Model-free feature screening for ultrahigh dimensional discriminant analysis. Journal of the American Statistical Association, 110, 630–641. doi: 10.1080/01621459.2014.920256
   PubMed Web of Science ®Google Scholar
• Daniels, M. J., & Hogan, J. W. (2008). Missing data in longitudinal studies: Strategies for Bayesian modeling and sensitivity analysis. London: Chapman and Hall.
   Google Scholar
• Daniels, M. J., Wang, C., & Marcus, B. H. (2014). Fully Bayesian inference under ignorable missingness in the presence of auxiliary covariates. Biometrics, 70, 62–72. doi: 10.1111/biom.12121
   Google Scholar
• Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B, 39, 1–38.
   Google Scholar
• Dey, D. K., Ghosh, S. K., & Lou, K. R. (1996). On local sensitivity measures in Bayesian analysis. Bayesian Robustness, 29, 21–40. doi: 10.1214/lnms/1215453059
   Google Scholar
• Ding, X. B., & Wang, Q. H (2011). Fusion-refinement procedure for dimension reduction with missing response at random. Journal of the American Statistical Association, 106, 1193–1207. doi: 10.1198/jasa.2011.tm10573
   Web of Science ®Google Scholar
• Fan, J., Feng, Y., & Song, R. (2011). Nonparametric independence screening in sparse ultra-high-dimensional additive models. Journal of the American Statistical Association, 106, 544–557. doi: 10.1198/jasa.2011.tm09779
   PubMed Web of Science ®Google Scholar
• Fan, J., & Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association, 96, 1348–1360. doi: 10.1198/016214501753382273
   Web of Science ®Google Scholar
• Fan, J., & Lv, J. C. (2008). Sure independence screening for ultrahigh dimensional feature space. Journal of the Royal Statistical Society: Series B, 70, 849–911. doi: 10.1111/j.1467-9868.2008.00674.x
   PubMed Web of Science ®Google Scholar
• Fan, J., & Peng, H. (2004). Nonconcave penalized likelihood with a diverging number of parameters. Annals of Statistics, 32, 928–961. doi: 10.1214/009053604000000256
   Web of Science ®Google Scholar
• Fang, F., & Shao, J. (2016). Model selection with nonignorable nonresponse. Biometrika, 103, 861–874. doi: 10.1093/biomet/asw039
   Web of Science ®Google Scholar
• Gelman, A. (1996). Inference and monitoring convergence. In W. R. Gilks, S. Richardson, & D. J. Speigelhalter (Eds.), Markov chain Monte Carlo in practice (pp. 131–144). London: Chapman and Hall.
   Google Scholar
• Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721–741. doi: 10.1109/TPAMI.1984.4767596
   PubMedGoogle Scholar
• Gustafson, P. (1996a). Local sensitivity of posterior expectations. Annals of Statistics, 24, 174–195. doi: 10.1214/aos/1033066205
   Web of Science ®Google Scholar
• Gustafson, P. (1996b). Local sensitivity of inferences to prior marginals. Journal of the American Statistical Association, 91, 774–781. doi: 10.1080/01621459.1996.10476945
   Web of Science ®Google Scholar
• Hansen, L. P. (1982). Large sample properties of generalised method of moments estimators. Econometrica, 50, 1029–1054. doi: 10.2307/1912775
   Web of Science ®Google Scholar
• Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57, 97–109. doi: 10.1093/biomet/57.1.97
   Web of Science ®Google Scholar
• Hens, N., Aerts, M., & Molenberghs, G. (2006). Model selection for incomplete and design-based samples. Statistics in Medicine, 25, 2502–2520. doi: 10.1002/sim.2559
   PubMed Web of Science ®Google Scholar
• Horvitz, D. G., & Thompson, D. J. (1952). A generalization of sampling without replacement from a finite universe. Journal of the American statistical Association, 47, 663–685. doi: 10.1080/01621459.1952.10483446
   Web of Science ®Google Scholar
• Ibrahim, J. G. (1990). Incomplete data in generalized linear models. Journal of the American Statistical Association, 85, 765–769. doi: 10.1080/01621459.1990.10474938
   Web of Science ®Google Scholar
• Ibrahim, J. G., Chen, M. H., & Lipsitz, S. R. (2001). Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika, 88, 551–564. doi: 10.1093/biomet/88.2.551
   Web of Science ®Google Scholar
• Ibrahim, J. G., Chen, M. H., Lipsitz, S. R., & Herring, A. H. (2005). Missing-data methods for generalized linear models. Journal of the American Statistical Association, 100, 332–346. doi: 10.1198/016214504000001844
   Web of Science ®Google Scholar
• Ibrahim, J. G., Zhu, H. T., & Tang, N. S. (2008). Model selection criterion for missing data problems using the EM algorithm. Journal of the American Statistical Association, 103, 1648–1658. doi: 10.1198/016214508000001057
   PubMed Web of Science ®Google Scholar
• Jansen, I., Molenberghs, G., Aerts, M., Thijs, H., & Van Steen, K. (2003). A local influence approach applied to binary data from a psychiatric study. Biometrics, 59, 410–419. doi: 10.1111/1541-0420.00048
   PubMed Web of Science ®Google Scholar
• Jiang, J. M., Nguyen, T., & Rao, J. S. (2015). The E-MS algorithm: Model selection with incomplete data. Journal of the American Statistical Association, 110, 1136–1147. doi: 10.1080/01621459.2014.948545
   Web of Science ®Google Scholar
• Jiang, D. P., Zhao, P. Y., & Tang, N. S. (2016). A propensity score adjustment method for regression models with nonignorable missing covariates. Computational Statistics and Data Analysis, 94, 98–119. doi: 10.1016/j.csda.2015.07.017
   Web of Science ®Google Scholar
• Jöreskog, K. G., & Sörbom, D. (1996). LISREL 8: Structural equation modeling with the SIMPLIS command language. Hover: Erlbaum.
   Google Scholar
• Kaciroti, N. A., & Raghunathan, T. (2014). Bayesian sensitivity analysis of incomplete data: Bridging pattern-mixture and selection models. Statistics in Medicine, 33, 4841–4857. doi: 10.1002/sim.6302
   Google Scholar
• Kim, J. K., & Shao, J. (2013). Statistical methods for handling incomplete data. Journal of Applied Statistics, 41, 2779–2780.
   Google Scholar
• Kim, J. K., & Yu, C. L. (2011). A semiparametric estimation of mean functionals with nonignorable missing data. Journal of the American Statistical Association, 106, 157–165. doi: 10.1198/jasa.2011.tm10104
   Web of Science ®Google Scholar
• Lai, P., Liu, Y. M., Liu, Z., & Wan, Y. (2017). Model free feature screening for ultrahigh dimensional data with responses missing at random. Computational Statistics and Data Analysis, 105, 201–216. doi: 10.1016/j.csda.2016.08.008
   Google Scholar
• Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38, 963–974. doi: 10.2307/2529876
   PubMed Web of Science ®Google Scholar
• Lam, C., & Fan, J. (2008). Profile-kernel likelihood inference with diverging number of parameters. Annals of Statistics, 36, 2232–2260. doi: 10.1214/07-AOS544
   PubMed Web of Science ®Google Scholar
• Lavine, M. (1991). Sensitivity in Bayesian statistics: The prior and the likelihood. Journal of the American Statistical Association, 86, 396–399. doi: 10.1080/01621459.1991.10475055
   Web of Science ®Google Scholar
• Lee, S. Y., & Tang, N. S. (2004). Local influence analysis of nonlinear structural equation models. Psychometrika, 69, 573–592. doi: 10.1007/BF02289856
   Web of Science ®Google Scholar
• Lee, S. Y., & Tang, N. S. (2006a). Bayesian analysis of structural equation models with mixed exponential family and ordered categorical data. British Journal of Mathematical and Statistical Psychology, 59, 151–172. doi: 10.1348/000711005X81403
   PubMed Web of Science ®Google Scholar
• Lee, S. Y., & Tang, N. S. (2006b). Bayesian analysis of nonlinear structural equation models with nonignorable missing data. Psychometrika, 71, 541–564. doi: 10.1007/s11336-006-1177-1
   Web of Science ®Google Scholar
• Lee, S. Y., & Tang, N. S. (2006c). Analysis of nonlinear structural equation models with nonignorable missing covariates and ordered categorical data. Statistica Sinica, 16, 1117–1141.
   Web of Science ®Google Scholar
• Lee, S. Y., & Zhu, H. T. (2002). Maximum likelihood estimation of nonlinear structural equation models. Psychometrika, 67, 189–210. doi: 10.1007/BF02294842
   Web of Science ®Google Scholar
• Leng, C., & Tang, C. Y. (2012). Penalized empirical likelihood and growing dimensional general estimating equations. Biometrika, 99, 703–716. doi: 10.1093/biomet/ass014
   Web of Science ®Google Scholar
• Lesaffre, E., & Verbeke, G. (1998). Local influence in linear mixed models. Biometrics, 54, 570–582. doi: 10.2307/3109764
   PubMed Web of Science ®Google Scholar
• Li, G., Peng, H., Zhang, J., & Zhu, L. X. (2012). Robust rank correlation based screening. The Annals of Statistics, 40, 1846–1877. doi: 10.1214/12-AOS1024
   Web of Science ®Google Scholar
• Li, G., Peng, H., & Zhu, L. (2011). Nonconcave penalized M-estimation with a diverging number of parameters. Statistica Sinica, 21, 391–419.
   Web of Science ®Google Scholar
• Li, R., Zhong, W., & Zhu, L. P. (2012). Feature screening via distance correlation learning. Journal of the American Statistical Association, 107, 1129–1139. doi: 10.1080/01621459.2012.695654
   PubMed Web of Science ®Google Scholar
• Liang, H., Wang, S., & Carroll, R. J. (2007). Partially linear models with missing response variables and error-prone covariates. Biometrika, 94, 185. doi: 10.1093/biomet/asm010
   PubMed Web of Science ®Google Scholar
• Linero, A. R., & Daniels, M. J. (2015). A flexible Bayesian approach to monotone missing data in longitudinal studies with nonignorable missingness with application to an acute Schizophrenia clinical trial. Journal of the American Statistical Association, 110, 45–55. doi: 10.1080/01621459.2014.969424
   Web of Science ®Google Scholar
• Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data. Hoboken, NJ: Wiley.
   Google Scholar
• Liu, T. Q., Lee, K. Y., & Zhao, H. Y. (2016). Ultrahigh dimensional feature selection via Kernel canonical correlation analysis. arXiv preprint arXiv:1604.07354.
   Google Scholar
• Liu, C., & Rubin, D. B. (1994). The ECME algorithm: A simple extension of em and ecm with faster monotone convergence. Biometrika, 81, 633–648. doi: 10.1093/biomet/81.4.633
   Web of Science ®Google Scholar
• Long, Q., & Johnson, B. A. (2015). Variable selection in the presence of missing data: Resampling and imputation. Biostatistics (Oxford, England), 16, 596–610. doi: 10.1093/biostatistics/kxv003
   Google Scholar
• Louis, T. A. (1982). Finding the observed information matrix when using the EM algorithm. Journal of the Royal Statistical Society. Series B, 44, 226–233.
   Web of Science ®Google Scholar
• McCullagh, P., & Nelder, J. A. (1989). Generalized linear models. London: Chapman & Hall.
   Google Scholar
• Meng, X. L., & Rubin, D. B. (1993). Maximum likelihood estimation via the ECM algorithm: A general framework. Biometrika, 80, 267–278. doi: 10.1093/biomet/80.2.267
   Web of Science ®Google Scholar
• Meng, X. L., & Schilling, S. (1996). Fitting full-information item factor models and an empirical investigation of bridge sampling. Journal of the American Statistical Association, 91, 1254–1267. doi: 10.1080/01621459.1996.10476995
   Web of Science ®Google Scholar
• Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state by fast computing machines. Journal of Chemical Physics, 21, 1087–1092. doi: 10.1063/1.1699114
   Google Scholar
• Millar, R. B., & Stewart, W. S. (2007). Assessment of locally influential observations in Bayesian models. Bayesian Analysis, 2, 365–383. doi: 10.1214/07-BA216
   Web of Science ®Google Scholar
• Muthén, L. K., & Muthén, B. O. (2017). Mplus user's guide (8th ed.). Los Angeles, CA: Muthén & Muthén.
   Google Scholar
• Owen, A. B. (1988). Empirical likelihood ratio confidence intervals for a single functional. Biometrika, 75, 237–249. doi: 10.1093/biomet/75.2.237
   Web of Science ®Google Scholar
• Owen, A. B. (2001). Empirical likelihood. New York: Chapman and Hall/CRC.
   Google Scholar
• Poon, W. Y., & Poon, Y. S. (1999). Conformal normal curvature and assessment of local influence. Journal of the Royal Statistical Society Series B, 61, 51–61. doi: 10.1111/1467-9868.00162
   Google Scholar
• Qin, J., Leung, D., & Shao, J. (2002). Estimation with survey data under nonignorable nonresponse or informative sampling. Journal of the American Statistical Association, 97, 193–200. doi: 10.1198/016214502753479338
   Web of Science ®Google Scholar
• Qin, J., Zhang, B., & Leung, D. H. Y. (2009). Empirical likelihood in missing data problems. Journal of the American Statistical Association, 104, 1492–1503. doi: 10.1198/jasa.2009.tm08163
   Web of Science ®Google Scholar
• Riddles, M. K. (2013). Propensity score adjusted method for missing data (Doctoral dissertation). Iowa State University.
   Google Scholar
• Riddles, M. K., Kim, J. K., & Im, J. (2016). A propensity-score-adjustment method for nonignorable nonresponse. Journal of Survey Statistics and Methodology, 4, 215–245. doi: 10.1093/jssam/smv047
   Google Scholar
• Robins, J. M., Rotnitzky, A., & Zhao, L. P. (1994). Estimation of regression coefficients when some regressors are not always observed. Journal of the American statistical Association, 89, 846–866. doi: 10.1080/01621459.1994.10476818
   Web of Science ®Google Scholar
• Rubin, D. B. (1976). Inference and missing data. Biometrika, 63, 581–592. doi: 10.1093/biomet/63.3.581
   Web of Science ®Google Scholar
• Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461–464. doi: 10.1214/aos/1176344136
   Web of Science ®Google Scholar
• Shao, J., & Wang, L. (2016). Semiparametric inverse propensity weighting for nonignorable missing data. Biometrika, 103, 175–187. doi: 10.1093/biomet/asv071
   Web of Science ®Google Scholar
• Shen, C. W., & Chen, Y. H. (2012). Model selection for generalized estimating equations accommodating dropout missingness. Biometrics, 68, 1046–1054. doi: 10.1111/j.1541-0420.2012.01758.x
   PubMed Web of Science ®Google Scholar
• Shen, C. W., & Chen, Y. H. (2013). Model selection of generalized estimating equations with multiply imputed longitudinal data. Biometrical Journal, 55, 899–911. doi: 10.1002/bimj.201200236
   PubMed Web of Science ®Google Scholar
• Shi, X. Y., Zhu, H. T., & Ibrahim, J. G. (2009). Local influence for generalized linear models with missing covariates. Biometrics, 65, 1164–1174. doi: 10.1111/j.1541-0420.2008.01179.x
   PubMed Web of Science ®Google Scholar
• Spieglhalter, D. J., Thomas, A., Best, N. G., & Lunn, D. (2003). WinBUGS user manual (Version 1.4). Cambridge: MRC Biostatistics Unit.
   Google Scholar
• Stute, W., Xue, L., & Zhu, L. (2007). Empirical likelihood inference in nonlinear errors-in-covariables models with validation data. Journal of the American Statistical Association, 102, 332–346. doi: 10.1198/016214506000000816
   Web of Science ®Google Scholar
• Tang, N. S., Chow, S. M., Ibrahim, J. G., & Zhu, H. T. (2017). Bayesian sensitivity analysis of a nonlinear dynamic factor analysis model with nonparametric prior and possible nonignorable missingness. Psychometrika, 82, 1–29. doi: 10.1007/s11336-017-9587-4
   Google Scholar
• Tang, N. S., & Tang, L. (2018). Estimation and variable selection in generalized partially nonlinear models with nonignorable missing responses. Statistics and its Interface, 11, 1–18. doi: 10.4310/SII.2018.v11.n1.a1
   Google Scholar
• Tang, N., Yan, X., & Zhao, P. (2018). Exponentially tilted likelihood inference on growing dimensional unconditional moment models. Journal of Econometrics, 202, 57–74. doi: 10.1016/j.jeconom.2017.08.018
   Web of Science ®Google Scholar
• Tang, N. S., & Zhao, P. Y. (2013). Empirical likelihood-based inference in nonlinear regression models with missing responses at random. Statistics, 47, 1141–1159. doi: 10.1080/02331888.2012.658807
   Web of Science ®Google Scholar
• Tang, N. S., Zhao, P. Y., & Zhu, H. T. (2014). Empirical likelihood for estimating equations with nonignorably missing data. Statistica Sinica, 24, 723.
   PubMed Web of Science ®Google Scholar
• Tanner, M. A., & Wong, W. H. (1987). The calculation of posterior distributions by data augmentation. Journal of the American statistical Association, 82, 528–540. doi: 10.1080/01621459.1987.10478458
   Web of Science ®Google Scholar
• Troxel, A. B., Ma, G., & Heitjan, D. F. (2004). An index of local sensitivity to nonignorability. Statistica Sinica, 14, 1221–1237.
   Web of Science ®Google Scholar
• Verbeke, G., Molenberghs, G., Thijs, H., Lesaffre, E., & Kenward, M. G. (2001). Sensitivity analysis for nonrandom dropout: A local influence approach. Biometrics, 57, 7–14. doi: 10.1111/j.0006-341X.2001.00007.x
   PubMed Web of Science ®Google Scholar
• Wang, D., & Chen, S. X (2009). Empirical likelihood for estimating equations with missing values. Annals of Statistics, 37, 490–517. doi: 10.1214/07-AOS585
   Web of Science ®Google Scholar
• Wang, Q. H., & Li, Y. J. (2018). How to make model-free feature screening approaches for full data applicable to the case of missing response? Scandinavian Journal of Statistics, 45, 324–346. doi: 10.1111/sjos.12290
   Google Scholar
• Wang, Q. H., & Qin, Y. S. (2010). Empirical likelihood confidence bands for distribution functions with missing responses. Journal of Statistical Planning and Inference, 140, 2778–2789. doi: 10.1016/j.jspi.2010.03.044
   Web of Science ®Google Scholar
• Wang, Q. H., & Rao, J. N. K. (2002). Empirical likelihood-based inference under imputation for missing response data. Annals of Statistics, 30, 896–924. doi: 10.1214/aos/1028674845
   Web of Science ®Google Scholar
• Wang, S., Shao, J., & Kim, J. K. (2014). An instrumental variable approach for identification and estimation with nonignorable nonresponse. Statistica Sinica, 24, 1097–1116.
   Web of Science ®Google Scholar
• Wei, B. C. (1998). Exponential family nonlinear models. Singapore: Springer-Verlag.
   Google Scholar
• Wei, Y., Ma, Y. Y., & Carroll, R. J. (2012). Multiple imputation in quantile regression. Biometrika, 99, 423–438. doi: 10.1093/biomet/ass007
   PubMed Web of Science ®Google Scholar
• Wei, G. G., & Tanner, M. (1990). A Monte Carlo implementation of the EM algorithm and the poor man's data augmentation algorithms. Publications of the American Statistical Association, 85, 699–704. doi: 10.1080/01621459.1990.10474930
   Google Scholar
• Xu, C., & Chen, J. H. (2014). The sparse MLE for ultrahigh-dimensional feature screening. Journal of the American Statistical Association, 109, 1257–1269. doi: 10.1080/01621459.2013.879531
   PubMed Web of Science ®Google Scholar
• Xue, L. (2009). Empirical likelihood for linear models with missing responses. Journal of Multivariate Analysis, 100, 1353–1366. doi: 10.1016/j.jmva.2008.12.009
   Web of Science ®Google Scholar
• Zhang, Y. Q., & Tang, N. S. (2017). Bayesian local influence analysis of general estimating equations with nonignorable missing data. Computational Statistics and Data Analysis, 105, 184–200. doi: 10.1016/j.csda.2016.08.010
   Web of Science ®Google Scholar
• Zhao, S. D. H., & Li, Y. (2014). Score test variable screening. Biometrics, 70, 862–871. doi: 10.1111/biom.12209
   Google Scholar
• Zhao, Y. Y., & Tang, N. S. (2015). Maximum-likelihood estimation and influence analysis in multivariate skew-normal reproductive dispersion mixed models for longitudinal data. Mathematische Operationsforschung Und Statistik, 49, 1348–1365.
   Google Scholar
• Zhao, P. Y., Tang, M. L., & Tang, N. S. (2013). Robust estimation of distribution functions and quantiles with non-ignorable missing data. Canadian Journal of Statistics, 41, 575–595. doi: 10.1002/cjs.11195
   Web of Science ®Google Scholar
• Zhao, H., Zhao, P. Y., & Tang, N. S. (2013). Empirical likelihood inference for mean functionals with nonignorably missing response data. Computational Statistics and Data Analysis, 66, 101–116. doi: 10.1016/j.csda.2013.03.023
   Google Scholar
• Zhu, H. T., Ibrahim, J. G., & Chen, M. H. (2015). Diagnostic measures for the cox regression model with missing covariates. Biometrika, 102, 907–923. doi: 10.1093/biomet/asv047
   PubMed Web of Science ®Google Scholar
• Zhu, H. T., Ibrahim, J. G., Cho, H., & Tang, N. S. (2012). Bayesian case influence measures for statistical models with missing data. Journal of Computational and Graphical Statistics, 21, 253–271. doi: 10.1198/jcgs.2011.10139
   PubMed Web of Science ®Google Scholar
• Zhu, H. T., Ibrahim, J. G., & Tang, N. S. (2011). Bayesian influence analysis: A geometric approach. Biometrika, 98, 307–323. doi: 10.1093/biomet/asr009
   PubMed Web of Science ®Google Scholar
• Zhu, H. T., Ibrahim, J. G., & Tang, N. S. (2014). Bayesian sensitivity analysis of statistical models with missing data. Statistica Sinica, 24, 871–896.
   Google Scholar
• Zhu, H. T., & Lee, S. Y. (2001). Local influence for incomplete-data models. Journal of the Royal Statistical Society, 63, 111–126. doi: 10.1111/1467-9868.00279
   Google Scholar
• Zhu, L. P., Li, L. X., Li, R. Z., & Zhu, L. X. (2011). Model-free feature screening for ultrahigh-dimensional data. Journal of the American Statistical Association, 106, 1464–1475. doi: 10.1198/jasa.2011.tm10563
   PubMed Web of Science ®Google Scholar
• Zou, H., & Li, R. Z. (2008). One-step sparse estimates in nonconcave penalized likelihood models. Annals of statistics, 36, 1509–1533. doi: 10.1214/009053607000000802
   PubMed Web of Science ®Google Scholar
• Zou, H., & Zhang, H. (2009). On the adaptive elastic-net with a diverging number of parameters. Annals of Statistics, 37, 1733–1751. doi: 10.1214/08-AOS625
   PubMed Web of Science ®Google Scholar