  We propose a novel modeling framework to study the effect of covariates of
various types on the conditional distribution of the response. The methodology
accommodates flexible model structure, allows for joint estimation of the
quantiles at all levels, and involves a computationally efficient estimation
algorithm. Extensive numerical investigation confirms good performance of the
proposed method. The methodology is motivated by and applied to a lactating sow
study, where the primary interest is to understand how the dynamic change of
minute-by-minute temperature in the farrowing rooms within a day (functional
covariate) is associated with low quantiles of feed intake of lactating sows,
while accounting for other sow-specific information (vector covariate).
