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

Maria Laura Battagliola (), Helle Sørensen, Anders Tolver and Ana-Maria Staicu
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Maria Laura Battagliola: École Polytechnique Fédérale de Lausanne
Helle Sørensen: University of Copenhagen
Anders Tolver: University of Copenhagen
Ana-Maria Staicu: North Carolina State University

Journal of Agricultural, Biological and Environmental Statistics, 2025, vol. 30, issue 1, No 10, 230 pages

Abstract: Abstract 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.

Keywords: Bootstrap; Clustered data; Subject-specific effects (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s13253-024-00601-5

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