High dimensional variable selection through group Lasso for multiple function‐on‐function linear regression: A case study in PM10 monitoring
Adelia Evangelista,
Christian Acal,
Ana M. Aguilera
et al.
Abstract:SummaryAnalyzing the effect of chemical and local meteorological variables over the behaviour in concentrations in the Abruzzo region (Italy), with the objective of forecasting and controlling air quality, motivates the current work. Given that the available data are curves that represent the day‐to‐day variations, a multiple function‐on‐function linear regression (MFFLR) model is considered. By assuming the Karhunen‐Loève expansion, MFFLR model can be reduced to a classical linear regression model for each p… Show more
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