ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2019
Additive quantile regression for clustered data with an application to children's physical activity.
Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Association between body mass index and physical fitness among multiethnic children aged 9-12 years in Zhejiang Province, China: a cross-sectional quantile regression analysis.Frontiers in public health · 2026Article
- Quantile Regression for Longitudinal Functional Data with Application to Feed Intake of Lactating Sows.Journal of agricultural, biological, and environmental statistics · 2025Article
- Adaptive aggregation for longitudinal quantile regression based on censored history process.Statistical methods in medical research · 2023Article
- Joint regression modelling of intensity and timing of accelerometer counts.Statistics in medicine · 2023Article
- Modified check loss for efficient estimation via model selection in quantile regression.Journal of applied statistics · 2021Article
- Modelling and estimation of nonlinear quantile regression with clustered data.Computational statistics & data analysis · 2019Article
- Quantile Regression Modeling of Latent Trajectory Features with Longitudinal Data.Journal of applied statistics · 2019Article
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Abstract
Additive models are flexible regression tools that handle linear as well as non-linear terms. The latter are typically modelled via smoothing splines. Additive mixed models extend additive models to include random terms when the data are sampled according to cluster designs (e.g. longitudinal).These models find applications in the study of phenomena like growth, certain disease mechanisms and energy expenditure in humans, when repeated measurements are available. We propose a novel additive mixed model for quantile regression. Our methods are motivated by an application to physical activity based on a data set with more than half a million accelerometer measurements in children of the UK Millennium Cohort Study. In a simulation study, we assess the proposed methods against existing alternatives.
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