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This article explores the application of quantile regression techniques to capture non-standard tail behaviours in spatially correlated data, typically encountered in environmental and climate sciences. In particular, we propose extensions of penalised spatial quantile regression models, to accommodate spatio-temporal data, as well as simultaneous estimates of spatial quantile surfaces. Through a real data application in the Lombardy region, we demonstrate the efficacy of the proposed models in analysing measurements of NO \(_2\) concentrations, showcasing the utility of quantile regression, where the spatial mean provides poor or little information on the phenomenon under study.

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Penalised Spatial Quantile Regression: Application to Air Quality Data

  • 会议论文

  • 发表日期：

    2025年

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  • Marco F. De Sanctis,
  • Ilenia Di Battista,
  • Eleonora Arnone,
  • Cristian Castiglione,
  • Mauro Bernardi,
  • Alessandro Palummo,
  • Laura M. Sangalli

摘要

This article explores the application of quantile regression techniques to capture non-standard tail behaviours in spatially correlated data, typically encountered in environmental and climate sciences. In particular, we propose extensions of penalised spatial quantile regression models, to accommodate spatio-temporal data, as well as simultaneous estimates of spatial quantile surfaces. Through a real data application in the Lombardy region, we demonstrate the efficacy of the proposed models in analysing measurements of NO \(_2\) concentrations, showcasing the utility of quantile regression, where the spatial mean provides poor or little information on the phenomenon under study.