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

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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 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.
Lingua originaleEnglish
Titolo della pubblicazione ospiteTopics in Theoretical and Applied Statistics
Pagine3-13
Numero di pagine11
Stato di pubblicazionePublished - 2016

Serie di pubblicazioni

NomeSTUDIES IN THEORETICAL AND APPLIED STATISTICS SELECTED PAPERS OF THE STATISTICAL SOCIETIES

All Science Journal Classification (ASJC) codes

  • Statistics and Probability

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