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Linear quantile mixed models. (English) Zbl 1325.62010

Summary: Dependent data arise in many studies. Frequently adopted sampling designs, such as cluster, multilevel, spatial, and repeated measures, may induce this dependence, which the analysis of the data needs to take into due account. In a previous publication [M. Geraci and M. Bottai, Biostatistics 8, No. 1, 140–154 (2007; Zbl 1170.62380)], we proposed a conditional quantile regression model for continuous responses where subjectspecific random intercepts were included to account for within-subject dependence in the context of longitudinal data analysis. The approach hinged upon the link existing between the minimization of weighted absolute deviations, typically used in quantile regression, and the maximization of a Laplace likelihood. Here, we consider an extension of those models to more complex dependence structures in the data, which are modeled by including multiple random effects in the linear conditional quantile functions. We also discuss estimation strategies to reduce the computational burden and inefficiency associated with the Monte Carlo EM algorithm we have proposed previously. In particular, the estimation of the fixed regression coefficients and of the random effects’ covariance matrix is based on a combination of Gaussian quadrature approximations and non-smooth optimization algorithms. Finally, a simulation study and a number of applications of our models are presented.

MSC:

62-07 Data analysis (statistics) (MSC2010)
65C60 Computational problems in statistics (MSC2010)
65C05 Monte Carlo methods
62P10 Applications of statistics to biology and medical sciences; meta analysis

Citations:

Zbl 1170.62380
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References:

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