Abstract
We consider the problem of regression analysis for data which consist of a large number of independent small groups or clusters of correlated observations. Instead of using the standard mean regression, we regress various percentiles of each marginal response variable over its covariates to obtain a more accurate assessment of the covariate effect. Our inference procedures are derived using the generalized estimating equations approach. The new proposal is robust and can be easily implemented. Graphical and numerical methods for checking the adequacy of the fitted quantile regression model are also proposed. The new methods are illustrated with an animal study in toxicology.
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Chen, L., Wei, LJ., Parzen, M.I. (2004). Quantile Regression for Correlated Observations. In: Lin, D.Y., Heagerty, P.J. (eds) Proceedings of the Second Seattle Symposium in Biostatistics. Lecture Notes in Statistics, vol 179. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9076-1_4
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DOI: https://doi.org/10.1007/978-1-4419-9076-1_4
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