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Quantile regression for longitudinal data

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  • Koenker, Roger

Abstract

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 modify this inflation effect. A general approach to estimating quantile regression models for longitudinal data is proposed employing l1 regularization methods. Sparse linear algebra and interior point methods for solving large linear programs are essential computational tools.

Suggested Citation

  • Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 74-89, October.
    • Handle: RePEc:eee:jmvana:v:91:y:2004:i:1:p:74-89
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    References listed on IDEAS

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    1. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    2. T. J. Cole, 1988. "Fitting Smoothed Centile Curves to Reference Data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 151(3), pages 385-406, May.
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