A moving average Cholesky factor model in covariance modeling for composite quantile regression with longitudinal data

DOI10.1016/j.csda.2017.02.015zbMath1464.62124OpenAlexW2597242268MaRDI QIDQ1654266

Yalian Li, Jing Lv, Hu Yang, Chaohui Guo

Publication date: 7 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2017.02.015

zbMATH Keywords

robustnesslongitudinal datamoving averagecomposite quantile regressionmodified Cholesky decompositionsmoothed estimating equation

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Estimation in multivariate analysis (62H12)

Related Items

Quantile regression estimation for distortion measurement error data, Robust statistical inference for longitudinal data with nonignorable dropouts, Smoothed quantile regression with nonignorable dropouts, Improved composite quantile regression and variable selection with nonignorable dropouts, Robust estimation via modified Cholesky decomposition for modal partially nonlinear models with longitudinal data, Improved empirical likelihood inference and variable selection for generalized linear models with longitudinal nonignorable dropouts, Generalized partial linear models with nonignorable dropouts, Quantile estimations via modified Cholesky decomposition for longitudinal single-index models, Improved kth power expectile regression with nonignorable dropouts

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