Imputation Strategies for Missing Data in Environmental Time Series for an Unlucky Situation
After a detailed review of the main specific solutions for treatment of missing data in environmental time series, this paper deals with the unlucky situation in which, in an hourly series, missing data immediately follow an absolutely anomalous period, for which we do not have any similar period to use for imputation. A tentative multivariate and multiple imputation is put forward and evaluated; it is based on the possibility, typical of environmental time series, to resort to correlations or physical laws that characterize relationships between air pollutants.
KeywordsMultiple Imputation ARMA Model Imputation Procedure Imputation Strategy Anomalous Period
Unable to display preview. Download preview PDF.
- LITTLE, R.J. and RUBIN, D.B. (1987): Statistical analysis with Missing Data. Wiley, New York.Google Scholar
- LOVISON, G. and MENDOLA (2003): Car-free Sundays and Air Pollution. Conference Book of Abstract of The ISI International Conference on Environmental Statistics and Health, Santiago de Compostela (Spain) July 16–18, 2003.Google Scholar
- MENDOLA, D. (2002): Road traffic restricions and air pollution in an urban area. A case study in Palermo. Working Paper GRASPA, n.15, http://www.graspa.org.Google Scholar
- MENDOLA, D. and LOVISON, G. (2002): Are car-free days effective. A case study in Palermo using Dynamic Linear Models. Invited Paper in TIES2002, The Annual Conference of The International Environmetric Society, Conference book of Abstracts, Genova, June 18–22, 2002.Google Scholar
- MENDOLA, D. and LOVISON, G. (2003): Multivariate Monitoring of Air Pollutants and of Effects of Meterological Conditions. Proceedings of SIS2003, Annual meeting of Italian Statistical Society.Google Scholar
- RUBIN, D.B. (1987): Multiple Imputation for Nonresponse in Surveys. Wiley, New York.Google Scholar
- SHUMWAY, R.H. and STOFFER, D.S. (1982): An approach to time series smooth-ing and forecating using EM algorithm. J. Time Series Anal., 3, 253–264.Google Scholar
- SHUMWAY, R.H. and STOFFER, D.S. (2000): Time Series Analysis and Its Applications, Springer, New York.Google Scholar
- VAN BUUREN, S. and OUDSHOORN, C.G.M. (1999): Flexible multivariate imputation by MICE TNO report PG/VGZ/99.054, TNO Prevention and Health, Leiden.Google Scholar
- VAN BUUREN, S. and OUDSHOORN, C.G.M. (2000): Multivariate Imputation by Chained Equations,-MICE V1.0 User’s manual. TNO report PG/VGZ/00.038, TNO Prevention and Health, Leiden.Google Scholar