Quantile Regression for Longitudinal Functional Data with Application to Feed Intake of Lactating Sows

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

This article focuses on the study of lactating sows, where the main interest is the influence of temperature, measured throughout the day, on the lower quantiles of the daily feed intake. We outline a model framework and estimation methodology for quantile regression in scenarios with longitudinal data and functional covariates. The quantile regression model uses a time-varying regression coefficient function to quantify the association between covariates and the quantile level of interest, and it includes subject-specific intercepts to incorporate within-subject dependence. Estimation relies on spline representations of the unknown coefficient functions and can be carried out with existing software. We introduce bootstrap procedures for bias adjustment and computation of standard errors. Analysis of the lactation data indicates, among others, that the influence of temperature increases during the lactation period.Supplementary materials accompanying this paper appear on-line.

TidsskriftJournal of Agricultural, Biological, and Environmental Statistics
StatusE-pub ahead of print - 2024

Bibliografisk note

Funding Information:
The authors also wish to acknowledge that revisions were carried out, while the first author was supported by the European Research Council under Grant CoG 2015-682172NETS, within the Seventh European Union Framework Program.

Funding Information:
Open access funding provided by EPFL Lausanne. The project was partly funded by the Danish Research Council (DFF Grant 7014-00221).

Publisher Copyright:
© The Author(s) 2024.

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