Environmetrics and Functional Data

 

The school has a strong research focus on the intersection of environmental statistics and functional data analysis. Ongoing projects address spatiotemporal processes in air quality, meteorological dynamics, and environmental risk, often using functional or compositional data representations. Functional hidden dynamic models, spatiotemporal clustering, and multivariate models for environmental sustainability indicators form part of this research. Collaborations include open-data initiatives such as AgrImOnIA, aimed at integrating livestock, pollution, and climate data to assess environmental impact. Emphasis is placed on linking statistical innovation with policy-relevant insights in sustainability, urban dynamics, and public health.

Researchers

Publications

  • Otto, P., Fassò, A., Maranzano, P. (2024): A review of regularised estimation methods and cross-validation in spatiotemporal statistics. Statistics Surveys. → Broad relevance across spatiotemporal environmental applications.
  • Otto, P., Fusta Moro, A., Rodeschini, J., et al. (2024): Spatiotemporal modelling of PM2.5 concentrations in Lombardy (Italy) – A comparative study. Environmental and Ecological Statistics. → Real-world comparative study using environmental data.
  • Fassò, A., Rodeschini, J., et al. incl. Otto, P. (2023): Agrimonia: a dataset on livestock, meteorology and air quality in the Lombardy region, Italy. Scientific Data. → Open data resource underpinning functional-environmetric analyses.
  • Otto, P., Piter, A., Gijsman, R. (2021): Statistical Analysis of Beach Profile Evolution and External Influences: Applying a Spatiotemporal Functional Approach. Coastal Engineering. → Illustrates functional spatiotemporal methods in coastal management and engineering.
  • Otto, P., Maranzano, P., Fassò, A. (2023): Adaptive LASSO estimation for functional hidden dynamic geostatistical models. Stochastic Environmental Research and Risk Assessment. → High-level contribution integrating functional data with spatiotemporal geostatistics.