  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.
