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 EmpiricalOrthogonal Function 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.

DI SALVO, F., Plaia, A., Ruggieri, M., & Agro', G. (2016). Empirical Orthogonal Function and Functional Data Analysis Procedures to Impute Long Gaps in Environmental Data. In Giorgio Alleva • Andrea Giommi (a cura di), Topics in Theoretical and Applied Statistics (pp. 3-13). Springer [10.1007/978-3-319-27274-0_1].

Empirical Orthogonal Function and Functional Data Analysis Procedures to Impute Long Gaps in Environmental Data

DI SALVO, Francesca;PLAIA, Antonella;RUGGIERI, Mariantonietta;AGRO', Gianna
2016

Abstract

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 EmpiricalOrthogonal Function 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.
Settore SECS-S/01 - Statistica
DI SALVO, F., Plaia, A., Ruggieri, M., & Agro', G. (2016). Empirical Orthogonal Function and Functional Data Analysis Procedures to Impute Long Gaps in Environmental Data. In Giorgio Alleva • Andrea Giommi (a cura di), Topics in Theoretical and Applied Statistics (pp. 3-13). Springer [10.1007/978-3-319-27274-0_1].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10447/202492
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