Conditional analysis for mixed covariates, with application to feed intake of lactating sows

TL;DR

This paper introduces a flexible modeling framework for analyzing how different types of covariates influence the entire conditional distribution of a response, demonstrated through a study on sow feed intake related to temperature changes.

Contribution

It develops a novel, computationally efficient method for joint estimation of quantiles with mixed covariates, applicable to complex real-world data.

Findings

Method performs well in numerical tests.

Applied successfully to sow feed intake data.

Reveals relationship between temperature and low feed intake quantiles.

Abstract

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).