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Quantile regression for correlated observations. (English) Zbl 1390.62109
Lin, D. Y. (ed.) et al., Proceedings of the second Seattle symposium in biostatistics. Analysis of correlated data, Seattle, WA, USA, November 20–21, 2000. New York, NY: Springer (ISBN 0-387-20862-3/pbk). Lecture Notes in Statistics 179, 51-69 (2004).
Summary: 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.
For the entire collection see [Zbl 1058.62104].

MSC:
62G08 Nonparametric regression and quantile regression
62H30 Classification and discrimination; cluster analysis (statistical aspects)
62G05 Nonparametric estimation
62P10 Applications of statistics to biology and medical sciences; meta analysis
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