Quantile Regression for Longitudinal Data
نویسندگان
چکیده
The penalized least squares interpretation of the classical random effects estimator suggests a possible way forward for quantile regression models with a large number of “fixed effects”. The introduction of a large number of individual fixed effects can significantly inflate the variability of estimates of other covariate effects. Regularization, or shrinkage of these individual effects toward a common value can help to mollify this inflation effect. A general approach to estimating quantile regression models for longitudinal data is proposed employing `1 regularization methods. Sparse linear algebra and interior point methods for solving large linear programs are essential computational tools.
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