Imputation of missing data in time series for air pollutants

Missing data are major concerns in epidemiological studies of the health effects of environmental air pollutants. This article presents an imputation-based method that is suitable for multivariate time series data, which uses the EM algorithm under the assumption of normal distribution. Different ap...

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Bibliographic Details
Published in:Atmospheric environment (1994) 2015-02, Vol.102, p.96-104
Main Authors: Junger, W.L., Ponce de Leon, A.
Format: Article
Language:English
Subjects:
Air pollution
Computer programs
Computer simulation
Data imputation
Data processing
EM algorithm
Environmental epidemiology
Filtering
Missing data
Packages
Particulate matter
Pollutants
Time series
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Summary:Missing data are major concerns in epidemiological studies of the health effects of environmental air pollutants. This article presents an imputation-based method that is suitable for multivariate time series data, which uses the EM algorithm under the assumption of normal distribution. Different approaches are considered for filtering the temporal component. A simulation study was performed to assess validity and performance of proposed method in comparison with some frequently used methods. Simulations showed that when the amount of missing data was as low as 5%, the complete data analysis yielded satisfactory results regardless of the generating mechanism of the missing data, whereas the validity began to degenerate when the proportion of missing values exceeded 10%. The proposed imputation method exhibited good accuracy and precision in different settings with respect to the patterns of missing observations. Most of the imputations obtained valid results, even under missing not at random. The methods proposed in this study are implemented as a package called mtsdi for the statistical software system R. •We propose a method for imputation of missing values in times series.•Simulations showed adequate goodness-of-fit.•The findings also suggest good accuracy and precision.•We implemented the method as an open source R library.
ISSN:1352-2310
1873-2844
DOI:10.1016/j.atmosenv.2014.11.049