NDLI: Composite hierachical linear quantile regression

Content Provider	Springer Nature Link
Author	Chen, Yan liang Yu, Ke ming Pan, Jian xin Tian, Mao zai
Copyright Year	2014
Abstract	Multilevel (hierarchical) modeling is a generalization of linear and generalized linear modeling in which regression coefficients are modeled through a model, whose parameters are also estimated from data. Multilevel model fails to fit well typically by the use of the EM algorithm once one of level error variance (like Cauchy distribution) tends to infinity. This paper proposes a composite multilevel to combine the nested structure of multilevel data and the robustness of the composite quantile regression, which greatly improves the efficiency and precision of the estimation. The new approach, which is based on the Gauss-Seidel iteration and takes a full advantage of the composite quantile regression and multilevel models, still works well when the error variance tends to infinity. We show that even the error distribution is normal, the MSE of the estimation of composite multilevel quantile regression models nearly equals to mean regression. When the error distribution is not normal, our method still enjoys great advantages in terms of estimation efficiency.
Ending Page	64
Page Count	16
Starting Page	49
File Format	PDF
ISSN	01689673
e-ISSN	16183932
Journal	Acta Mathematicae Applicatae Sinica
Issue Number	1
Volume Number	30
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-04-26
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	random effects multilevel model Asymptotic properties Estimation Theoretical, Mathematical and Computational Physics E-CQ algorithm, fixed effects composite quantile regression Martingales with discrete parameter Applications of Mathematics Math Applications in Computer Science
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics

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