Generalised linear model-based algorithm for detection of outliers in environmental data and comparison with semi-parametric outlier detection methods

Abrutzky, 2012, Health effects of climate and air pollution in buenos aires: a first time series analysis, J. Environ. Protect., 3, 262, 10.4236/jep.2012.33033 Agresti, 2002 Akaike, 1974, A new look at the statistical model identification, IEEE Trans. Automat. Contr., 19, 716, 10.1109/TAC.1974.1100705 Araki, 2017, Effect of spatial outliers on the regression modelling of air pollutant concentrations: a case study in Japan, Atmos. Environ., 153, 83, 10.1016/j.atmosenv.2016.12.057 Baffi, 1999, Non-linear projection to latent structures revisited (the neural network PLS algorithm), Comput. Chem. Eng., 23, 1293, 10.1016/S0098-1354(99)00291-4 Bao, 2009, Partial least squares with outlier detection in spectral analysis: a tool to predict gasoline properties, Fuel, 88, 1216, 10.1016/j.fuel.2008.11.025 Barnett, 2004 Barnett, 1978 Beirlant, 2004 Ben-Gal, 2005, Outlier detection, 117 Bobia, 2015, Spatial outlier detection in the PM10 monitoring network of Normandy (France), Atmo. Pollut. Res., 6, 476, 10.5094/APR.2015.053 Brněnské komunikace, 2010 Brněnské komunikace, 2011 Brněnské komunikace, 2012 Brněnské komunikace, 2013 Brněnské komunikace, 2014 Brněnské komunikace, 2015 Brněnské komunikace, 2016 Burman, 1988 Čampulová, 2018, Comparison of methods for smoothing environmental data with an application to particulate matter PM10, Acta Univ. Agric. Silvic. Mendelianae Brunensis, 66, 453, 10.11118/actaun201866020453 Čampulová, 2017, Control chart and six sigma based algorithms for identification of outliers in experimental data, with an application to particulate matter PM10, Atmos. Pollut. Res., 8, 700, 10.1016/j.apr.2017.01.004 Čampulová, 2018, Outlier detection in PM10 aerosols by generalised linear model Čampulová, 2018, Algorithm for identification of outliers in environmental data, J. Chemometr., 32, 1, 10.1002/cem.2997 Chaloulakou, 2003, Measurements of PM10 and PM2.5 particle concentrations in athens, Greece, Atmos. Environ., 37, 649, 10.1016/S1352-2310(02)00898-1 Chandola, 2009, Anomaly detection: a survey, ACM Comput. Surv., 41, 58, 10.1145/1541880.1541882 EEA (European Environment Agency), 2017 EEA (European Environment Agency), 2018 EU, 2008, Directive 2008/50/ec of the European Parliament and of the Council of 21 may 2008 on ambient air quality and cleaner air for Europe, Off. J. Eur. Commun. L, 152, 1 Fawcett, 2016, Sea-surge and wind speed extremes: optimal estimation strategies for planners and engineers, Stoch. Environ. Res. Risk Assess., 30, 463, 10.1007/s00477-015-1132-3 Filzmoser, 2005, Identification of multivariate outliers: a performance study, Aust. J. Stat., 34, 127, 10.17713/ajs.v34i2.406 Fox, 1972, Outliers in time series, J. Roy. Stat. Soc. Ser. B, 34, 350 Garces, 2011, Outliers detection in environmental monitoring databases, Eng. Appl. Artif. Intell., 24, 341, 10.1016/j.engappai.2010.10.018 Gomes, 1993, On the estimation of parameter of rare events in environmental time series, 225 Gupta, 2014, Outlier detection for temporal data: a survey, IEEE T. Knowl. Data Eng., 26, 2250, 10.1109/TKDE.2013.184 Hartigan, 1979, A k-means clustering algorithm, Appl. Stat., 28, 10.2307/2346830 Holešovský, 2018, Semiparametric outlier detection in nonstationary times series: case study for atmospheric pollution in Brno, Czech Republic, Atmos. Pollut. Res., 9, 27, 10.1016/j.apr.2017.06.005 Hormann, 2005, Analysis and prediction of particulate matter PM10 for the winter season in Graz, Aust. J. Stat., 34, 307, 10.17713/ajs.v34i4.420 Hrdličková, 2008, Identification of factors affecting air pollution by dust aerosol PM10 in Brno City, Czech Republic, Atmos. Pollut. Res., 42, 8661 Hübnerová, 2014, Analysis of daily average PM10 predictions by generalized linear models in Brno, Czech Republic, Atmos. Pollut. Res., 5, 471, 10.5094/APR.2014.055 Iglewicz, 1993, The ASQC basic references in quality control: statistical techniques, vol. 16 Johnson, 1992 Kim, 2015, A review on the human health impact of airborne particulate matter, Environ. Int., 74, 136, 10.1016/j.envint.2014.10.005 Křůmal, 2017, Characterization of organic compounds in winter PM1 aerosols in a small industrial town, Atmos. Pollut. Res., 8, 930, 10.1016/j.apr.2017.03.003 Lourenço, 2014, M-regression, false discovery rates and outlier detection with application to genetic association studies, Comput. Stat. Data Anal., 78, 33, 10.1016/j.csda.2014.03.019 McCullagh, 1989 McLachlan, 2008 Mikuška, 2017, Seasonal variability of monosaccharide anhydrides, resin acids,methoxyphenols and saccharides in PM2.5 in Brno, the Czech Republic, Atmos. Pollut. Res., 8, 576, 10.1016/j.apr.2016.12.018 Miller, 2010, Intra-urban correlation and spatial variability of air toxics across an international airshed in Detroit, Michigan (USA) and Windsor, Ontario (Canada), Atmos. Environ., 44, 1162, 10.1016/j.atmosenv.2009.12.030 O'Leary, 2014, Modeling spatiotemporal variability of intra-urban air pollutants in Detroit: a pragmatic approach, Atmos. Environ., 94, 417, 10.1016/j.atmosenv.2014.05.010 O'Leary, 2016, Identification and influence of spatio-temporal outliers in urban air quality measurements, Sci. Total Environ., 573, 55, 10.1016/j.scitotenv.2016.08.031 Pope, 2006, Health effects of fine particulate air pollution: lines that connect, J. Air Waste Manag. Assoc., 56, 709, 10.1080/10473289.2006.10464485 Pope, 1995, Review of epidemiological evidence of health effects of particulate air pollution, Inhal. Toxicol., 7, 1, 10.3109/08958379509014267 Rahman, 2012, Multiple linear regression models in outlier detection, Int. J. Res. Comput. Sci., 2, 23, 10.7815/ijorcs.22.2012.018 Restrepo, 2012, Asthma hospital admissions and ambient air pollutant concentrations in New York city, J. Environ. Protect., 3, 1102, 10.4236/jep.2012.329129 Rice, 2006, A simple diagnostic plot connecting robust estimation, outlier detection, and false discovery rates, J. Appl. Stat., 33, 1131, 10.1080/02664760600747002 Ripley, 1999 Shaadan, 2015, Anomaly detection and assessment of PM10 functional data at several locations in the klang valley, Malaysia, Atmos. Pollut. Res., 6, 365, 10.5094/APR.2015.040 She, 2011, Outlier detection using nonconvex penalized regression, J. Am. Stat. Assoc., 106, 626, 10.1198/jasa.2011.tm10390 Silva, 2016, On some aspects of peaks over-threshold modeling of oods under nonstationarity using climate covariates, Stoch. Environ. Res. Risk Assess., 30, 207, 10.1007/s00477-015-1072-y Stadlober, 2008, Quality and performance of a PM10 daily forecasting model, Atmos. Environ., 42, 1098, 10.1016/j.atmosenv.2007.10.073 Stadlober, 2012, Prediction and forecast of daily PM10 concentrations in Brno and Graz by different regression approaches, Aust. J. Stat., 41, 287, 10.17713/ajs.v41i4.169 WHO, 2005