GAMs and functional kriging for air quality data

Risultato della ricerca: Conference contribution

Abstract

Data having spatio-temporal structure are often observed in environmental sciences. They may be considered as discrete observations from curves along time and/or space and treated as functional. Generalized Additive Models (GAMs)represent a useful tool for modelling, for example, as pollutant concentrations describing their spatial and/or temporal trends.Usually, the prediction of a curve at an unmonitored site is necessary and, with this aim, we extend kriging for functional data to a multivariate context. Moreover, even if we are interested only in predicting a single pollutant, such as PM10, the estimation can be improved exploiting its correlation with the other pollutants. Cross validation is used to test the performance of the proposed procedure.
Lingua originaleEnglish
Titolo della pubblicazione ospitePROCEEDINGS of the 48th scientific meeting of the Italian Statistical Society
Numero di pagine6
Stato di pubblicazionePublished - 2016

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