@inbook{323ddbda042c48738d97e88058d0a6ae,
title = "Empirical Orthogonal Function and Functional Data Analysis Procedures to Impute Long Gaps in Environmental Data",
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.",
keywords = "EOF, Environmental data, FDA, Missing data, EOF, Environmental data, FDA, Missing data",
author = "{Di Salvo}, Francesca and Gianna Agro' and Antonella Plaia and Mariantonietta Ruggieri",
year = "2016",
language = "English",
isbn = "978-3-319-27274-0",
series = "STUDIES IN THEORETICAL AND APPLIED STATISTICS SELECTED PAPERS OF THE STATISTICAL SOCIETIES",
pages = "3--13",
booktitle = "Topics in Theoretical and Applied Statistics",
}