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July 18, 2025Journal of the Royal Statistical Society Series C (Applied Statistics)Open Access

Functional Gaussian graphical regression models for air quality data

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Authors

RFRita FiciGSGianluca SottileLALuigi Augugliaro

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Overview

This analysis reveals spatial interactions of atmospheric chemicals in air quality data, suggesting new modeling approaches.

Key Points

  • Quantifying functional dependencies and interdependencies among responses is critical for accurate analysis.
  • We introduced a functional Gaussian graphical regression model with a new Kullback–Leibler cross-validation method.
  • This approach accommodates multivariate response functions and enhances model inference capabilities in complex datasets.
  • Our findings indicate improved model performance, particularly in capturing spatial dynamics of air pollution.

Cite This Study

Fici et al. (2025) studied this question.

synapsesocial.com/papers/68af59ddad7bf08b1eade8behttps://doi.org/10.1093/jrsssc/qlaf042
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