Improved kth power expectile regression with nonignorable dropouts

References

• D.J. Aigner, T. Amemiya, and D.J. Poirier, On the estimation of production frontiers: Maximum likelihood estimation of the parameters of a discontinuous density function, Int. Econ. Rev. (Philadelphia) 17 (1976), pp. 377–396.
   Google Scholar
• Y. Bai, W.K. Fung, and Z. Zhu, Weighted empirical likelihood for generalized linear models with longitudinal data, J. Statist. Plann. Inference. 140 (2010), pp. 3446–3456.
   Web of Science ®Google Scholar
• H. Cho, H.G. Hong, and M.O. Kim, Efficient quantile marginal regression for longitudinal data with dropouts, Biostatistics 17 (2016), pp. 561–575.
   PubMed Web of Science ®Google Scholar
• H. Cho and A. Qu, Efficient estimation for longitudinal data by combining large-dimensional moment conditions, Electron. J. Stat. 9 (2015), pp. 1315–1334.
   Web of Science ®Google Scholar
• G.M. Fitzmaurice, G. Molenberghs, and S.R. Lipsitz, Regression models for longitudinal binary responses with informative drop-outs, J. R. Stat. Soc. 57 (1995), pp. 691–704.
   Google Scholar
• L. Fu and Y. Wang, Quantile regression for longitudinal data with a working correlation model, Comput. Statist. Data Anal. 56 (2012), pp. 2526–2538.
   Web of Science ®Google Scholar
• L.P. Hansen, Large sample properties of generalized method of moments estimators, Econometrica 50 (1982), pp. 1029–1054.
   Web of Science ®Google Scholar
• J. Huang, L. Liu, and N. Liu, Estimation of large covariance matrices of longitudinal data with basis function approximations, J. Comput. Graph. Stat. 16 (2007), pp. 189–209.
   Web of Science ®Google Scholar
• Y. Jiang, F. Lin, and Y. Zhou, The kth power expectile regression, Ann. Inst. Stat. Math. 73 (2021), pp. 83–113.
   Web of Science ®Google Scholar
• J.K. Kim and C.L. Yu, A semiparametric estimation of mean functionals with nonignorable missing data, J. Amer. Statist. Assoc. 106 (2011), pp. 157–165.
   Web of Science ®Google Scholar
• R. Koenker and J.G. Bassett, Regression quantiles, Econometrica 46 (1978), pp. 33–50.
   Web of Science ®Google Scholar
• C. Leng, W. Zhang, and J. Pan, Semiparametric mean-covariance regression analysis for longitudinal data, J. Amer. Statist. Assoc. 105 (2010), pp. 181–193.
   Web of Science ®Google Scholar
• K. Liang and S. Zeger, Longitudinal data analysis using generalized linear models, Biometrika 73 (1986), pp. 13–22.
   Web of Science ®Google Scholar
• R.J.A. Little and D.B. Rubin, Statistical Analysis with Missing Data, 2nd ed.Wiley, New York, 2002.
   Google Scholar
• J. Lv, C. Guo, H. Yang, and Y. Li, A moving average Cholesky factor model in covariance modeling for composite quantile regression with longitudinal data, Comput. Statist. Data Anal. 112 (2017), pp. 129–144.
   Web of Science ®Google Scholar
• W. Miao and E.J. Tchetgen Tchetgen, Identification and inference with nonignorable missing covariate data, Statist. Sinica 28 (2018), pp. 2049–2067.
   PubMed Web of Science ®Google Scholar
• G. Molenberghs and M. Kenward, Missing Data in Clinical Studies, John Wiley and Sons, Chichester, 2007.
   Google Scholar
• W.K. Newey and J.L. Powell, Asymmetric least squares estimation and testing, Econometrica 55 (1987), pp. 819–847.
   Web of Science ®Google Scholar
• A. Owen, Empirical likelihood confidence regions, Ann. Stat. 18 (1990), pp. 90–120.
   Web of Science ®Google Scholar
• J. Qin and J. Lawless, Empirical likelihood and general estimating equations, Ann. Stat. 22 (1994), pp. 300–325.
   Web of Science ®Google Scholar
• A. Qu, B.G. Lindsay, and B. Li, Improving generalised estimating equations using quadratic inference functions, Biometrika 87 (2000), pp. 823–836.
   Web of Science ®Google Scholar
• J.N.K. Rao and A.J. Scott, The analysis of categorical data from complex sample surveys: Chi-squared tests for goodness of fit and independence in two-way tables, J. Amer. Statist. Assoc. 76 (1981), pp. 221–230.
   Web of Science ®Google Scholar
• J.M. Robins and Y. Ritov, Toward a curse of dimensionality appropriate (CODA) asymptotic theory for semi-parametric models, Stat. Med. 16 (1997), pp. 285–319.
   PubMed Web of Science ®Google Scholar
• J.M. Robins, A. Rotnitzky, and L. Zhao, Estimation of regression coefficients when some regressors are not always observed, J. Am. Stat. Assoc. 89 (1994), pp. 846–866.
   Web of Science ®Google Scholar
• J. Shao and L. Wang, Semiparametric inverse propensity weighting for nonignorable missing data, Biometrika 103 (2016), pp. 175–187.
   Web of Science ®Google Scholar
• G. Tang, R.J.A. Little, and T.E. Raghunathan, Analysis of multivariate missing data with nonignorable nonresponse, Biometrika 90 (2003), pp. 747–764.
   Web of Science ®Google Scholar
• Y. Tang, Y. Wang, J. Li, and W. Qian, Improving estimation efficiency in quantile regression with longitudinal data, J. Statist. Plann. Inference. 165 (2015), pp. 38–55.
   Web of Science ®Google Scholar
• L. Wang, C. Qi, and J. Shao, Model-assisted regression estimators for longitudinal data with nonignorable dropout, Int. Stat. Rev. 87 (2019), pp. S121–S138.
   Web of Science ®Google Scholar
• S. Wang, J. Shao, and J.K. Kim, An instrumental variable approach for identification and estimation with nonignorable nonresponse, Statist. Sinica 24 (2014), pp. 1097–1116.
   Web of Science ®Google Scholar
• L. Xu, M.L. Tang, and Z. Chen, Analysis of longitudinal data by combining multiple dynamic covariance models, Stat. Interface. 12 (2019), pp. 479–487.
   Web of Science ®Google Scholar
• Y. Yuan and G. Yin, Bayesian quantile regression for longitudinal studies with nonignorable missing data, Biometrics 66 (2010), pp. 105–114.
   PubMed Web of Science ®Google Scholar
• W. Zhang and C. Leng, A moving average Cholesky factor model in covariance modelling for longitudinal data, Biometrika 99 (2011), pp. 141–150.
   Web of Science ®Google Scholar
• W. Zhang, C. Leng, and C.Y. Tang, A joint modelling approach for longitudinal studies, J. R. Stat. Soc. Ser. B (Stat. Methodol.) 77 (2015), pp. 219–238.
   Web of Science ®Google Scholar