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.

翻译：本文聚焦于哺乳母猪的研究，主要关注点在于全天测量的温度对每日采食量下分位数的影响。我们针对具有纵向数据和函数型协变量的场景，提出了一个分位数回归的模型框架与估计方法。该分位数回归模型采用时变回归系数函数来量化协变量与感兴趣分位数水平之间的关联，并包含个体特异性截距以纳入个体内相关性。估计过程基于未知系数函数的样条表示，可通过现有软件实现。我们引入了用于偏差校正和标准误计算的Bootstrap程序。对哺乳期数据的分析表明，哺乳期间温度对采食量的影响逐渐增强。