Air pollution data sets are usually spatio-temporal multivariate data related to time series of different pollutants recorded by a monitoring network. To improve the estimate of functional data when missing values, and mainly long gaps, are present in the original data set, some procedures are here proposed considering jointly FunctionalData Analysis and EmpiricalOrthogonalFunction approaches. In order to compare and validate the proposed procedures, a simulation plan is carried out and some performance indicators are computed.The obtained results show that one of the proposed procedures works better than the others, providing a better reconstruction especially in presence of long gaps.
|Titolo della pubblicazione ospite||Topics in Theoretical and Applied Statistics|
|Numero di pagine||11|
|Stato di pubblicazione||Published - 2016|
|Nome||STUDIES IN THEORETICAL AND APPLIED STATISTICS
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