Robust and smoothing variable selection for quantile regression models with longitudinal data

References

• Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Ser B. 1996;58(1):267–288.
   Google Scholar
• Frank LE, Friedman JH. A statistical view of some chemometrics regression tools. Technometrics. 1993;35(2):109–135.
   Web of Science ®Google Scholar
• Fan JQ, Li RZ. Variable selection via nonconcave penalized likelihood and its oracle properties. J Am Stat Assoc. 2001;96(456):1348–1360.
   Web of Science ®Google Scholar
• Zou H. The adaptive lasso and its oracle properties. J Am Stat Assoc. 2006;101(476):1418–1429.
   Web of Science ®Google Scholar
• Hunter DR, Li RZ. Variable selection using MM algorithms. Ann Stat. 2005;33(4):1617–1642.
   PubMed Web of Science ®Google Scholar
• Koenker R, Bassett Jr G. Regression quantiles. Econ J Econ Soc. 1978;46(1):33–50.
   Google Scholar
• Wu YC, Liu Y. Variable selection in quantile regression. Stat Sin. 2009;19(2):801–817.
   Web of Science ®Google Scholar
• Zou H, Li RZ. One-step sparse estimates in nonconcave penalized likelihood models. Ann Stat. 2008;36(4):1509–1533.
   PubMed Web of Science ®Google Scholar
• Peng B, Wang L. An iterative coordinate descent algorithm for high-dimensional nonconvex penalized quantile regression. J Comput Graph Stat. 2015;24(3):676–694.
   Web of Science ®Google Scholar
• Fan JQ, Fan YY, Barut E. Adaptive robust variable selection. Ann Stat. 2014;42(1):324–351.
   PubMed Web of Science ®Google Scholar
• Kato K. Estimation in functional linear quantile regression. Ann Stat. 2012;40(6):3108–3136.
   Web of Science ®Google Scholar
• Ma H, Li T, Zhu H, et al. Quantile regression for functional partially linear model in ultra-high dimensions. Comput Stat Data Anal. 2019;129:135–147.
   Web of Science ®Google Scholar
• Zhang YY, Lian H, Li GD, et al. Function additive quantile regression. Stat Sin. 2021;31:1–21.
   Web of Science ®Google Scholar
• Diggle PJ, Heagerty P, Liang K-Y, et al. Analysis of longitudinal data. Oxford: Oxford University Press; 2002.
   Google Scholar
• Fan JQ, Li RZ. New estimation and model selection procedures for semiparametric modeling in longitudinal data analysis. J Am Stat Assoc. 2004;99(467):710–723.
   Web of Science ®Google Scholar
• Wang LF, Li HZ, Huang JH. Variable selection in nonparametric varying-coefficient models for analysis of repeated measurements. J Am Stat Assoc. 2008;103(484):1556–1569.
   PubMed Web of Science ®Google Scholar
• Ni X, Zhang DW, Zhang HH. Variable selection for semiparametric mixed models in longitudinal studies. Biometrics. 2010;66(1):79–88.
   PubMed Web of Science ®Google Scholar
• Xu P, Wu P, Wang Y, et al. A GEE based shrinkage estimation for the generalized linear model in longitudinal data analysis. Hong Kong: Department of Mathematics, Hong Kong Baptist University; 2010.
   Google Scholar
• Johnson BA, Lin DY, Zeng DL. Penalized estimating functions and variable selection in semiparametric regression models. J Am Stat Assoc. 2008;103(482):672–680.
   PubMed Web of Science ®Google Scholar
• Wang L, Zhou JH, Qu A. Penalized generalized estimating equations for high-dimensional longitudinal data analysis. Biometrics. 2012;68(2):353–360.
   PubMed Web of Science ®Google Scholar
• Koenker R. Quantile regression for longitudinal data. J Multivar Anal. 2004;91(1):74–89.
   Web of Science ®Google Scholar
• Wang HJ, Fygenson M. Inference for censored quantile regression models in longitudinal studies. Ann Stat. 2009;37(2):756–781.
   Web of Science ®Google Scholar
• Leng C, Zhang W. Smoothing combined estimating equations in quantile regression for longitudinal data. Stat Comput. 2014;24(1):123–136.
   Web of Science ®Google Scholar
• Zhang Y, Wang H, Zhu Z. Quantile-regression-based clustering for panel data. J Econ. 2019;213(1):54–67.
   Web of Science ®Google Scholar
• Gao XL, Liu Q. Sparsity identification in ultra-high dimensional quantile regression models with longitudinal data. Commun Stat Theory Methods. 2020;49(19):4712–4736.
   Web of Science ®Google Scholar
• Fu LY, Wang Y-G. Quantile regression for longitudinal data with a working correlation model. Comput Stat Data Anal. 2012;56(8):2526–2538.
   Web of Science ®Google Scholar
• Wang N. Marginal nonparametric kernel regression accounting for within-subject correlation. Biometrika. 2003;90(1):43–52.
   Web of Science ®Google Scholar
• Zhu ZY, Fung WK, He XM. On the asymptotic of marginal regression splines with longitudinal data. Biometrika. 2008;95(4):907–917.
   Web of Science ®Google Scholar
• Chen L, Wei L-J, Parzen MI. Quantile regression for correlated observations. In: Proceedings of the Second Seattle Symposium in Biostatistics; 2004; Seattle. p. 51–69.
   Google Scholar
• Terpstra JT, McKean JW. Rank-based analysis of linear models using R. J Stat Softw. 2005;14(7):1–26.
   Web of Science ®Google Scholar
• Rousseeuw PJ, Driessen KV. A fast algorithm for the minimum covariance determinant estimator. Technometrics. 1999;41(3):212–223.
   Web of Science ®Google Scholar
• Fu LY, Wang Y-G. Efficient parameter estimation via Gaussian copulas for quantile regression with longitudinal data. J Multivar Anal. 2016;143:492–502.
   Web of Science ®Google Scholar
• Wang Y-G, Carey V. Working correlation structure misspecification, estimation and covariate design: implications for generalised estimating equations performance. Biometrika. 2003;90(1):29–41.
   Web of Science ®Google Scholar
• Heller G. Smoothed rank regression with censored data. J Am Stat Assoc. 2007;102(478):552–559.
   Web of Science ®Google Scholar
• Wang HS, Li B, Leng C. Shrinkage tuning parameter selection with a diverging number of parameters. J R Stat Soc Ser B. 2009;71(3):671–683.
   Google Scholar
• Spellman PT, Sherlock G, Zhang MQ, et al. Comprehensive identification of cell cycle–regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell. 1998;9(12):3273–3297.
   PubMed Web of Science ®Google Scholar
• Wang LF, Chen G, Li HZ. Group SCAD regression analysis for microarray time course gene expression data. Bioinformatics. 2007;23(12):1486–1494.
   PubMed Web of Science ®Google Scholar
• Pollard D. Empirical processes: theory and applications. Hayward, CA: Institute of Mathematical Statistics; 1990.
   Google Scholar
• Jung S-H. Quasi-likelihood for median regression models. J Am Stat Assoc. 1996;91(433):251–257.
   Web of Science ®Google Scholar