Probabilistic Automatic Outlier Detection for Surface Air Quality Measurements from the China National Environmental Monitoring Network
Author:
Publisher
Springer Science and Business Media LLC
Subject
Atmospheric Science
Link
http://link.springer.com/article/10.1007/s00376-018-8067-9/fulltext.html
Reference34 articles.
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3. Bickel, P. J., and E. Levina, 2008: Regularized estimation of large covariance matrices. The Annals of Statistics, 36, 199–227, https://doi.org/10.1214/009053607000000758 .
4. Bobbia, M., M. Misiti, Y. Misiti, J.-M. Poggi, and B. Portier, 2015: Spatial outlier detection in the PM10 monitoring network of Normandy (France). Atmospheric Pollution Research, 6, 476–483, https://doi.org/10.5094/APR.2015.053 .
5. Campulová, M., P. Veselík, and J. Michálek, 2017: Control chart and Six sigma based algorithms for identification of outliers in experimental data, with an application to particulate matter PM10. Atmospheric Pollution Research, 8, 700–708, https://doi.org/10.1016/j.apr.2017.01.004 .
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