[1] W. J. Dixon. Analysis of extreme values. The Annals of Mathematical Statistics, vol. 21, no. 4, pp. 488–506, 1950. DOI:  10.1214/aoms/1177729747.
[2] H. Wainer. Robust statistics: A survey and some prescriptions. Journal of Educational Statistics, vol. 1, no. 4, pp. 285–312, 1976. DOI:  10.3102/10769986001004285.
[3] D. M. Hawkins. Identification of Outliers, Dordrecht, The Netherlands: Springer, 1980. DOI: 10.1007/978-94-015-3994-4.
[4] R. A. Johnson, D. W. Wirchern. Applied Multivariate Statistical Analysis, 3rd ed., Englewood Cliffs, USA: Prentice-Hall, 1992.
[5] V. Barnett, T. Lewis. Outliers in Statistical Data, 3rd ed., Chichester, UK: Wiley, 1994.
[6] J. R. Xue, J. W. Fang, P. Zhang. A survey of scene understanding by event reasoning in autonomous driving. International Journal of Automation and Computing, vol. 15, no. 3, pp. 249–266, 2018. DOI:  10.1007/s11633-018-1126-y.
[7] J. W. Osborne, A. Overbay. The power of outliers (and why researchers should always check for them). Practical Assessment, Research, and Evaluation, vol. 9, no. 9, Article number 6, 2004.
[8] N. N. Taleb. Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications. USA: STEM Academic Press, 2020.
[9] P. J. Rousseeuw, A. M. Leroy. Robust Regression and Outlier Detection, New York, USA: John Wiley & Sons, 1987.
[10] I. Ben-Gal. Outlier detection. Data Mining and Knowledge Discovery Handbook, O. Maimon, L. Rokach, Eds., Boston, USA: Springer, pp. 131–146, 2005. DOI: 10.1007/0-387-25465-X_7.
[11] B. Iglewicz, D. C. Hoaglin. How to Detect and Handle Outliers, Milwaukee, USA: ASQ Quality Press, 1993.
[12] J. W. Kantelhardt, S. A. Zschiegner, E. Koscielny-Bunde, S. Havlin, A. Bunde, H. E. Stanley. Multifractal detrended fluctuation analysis of nonstationary time series. Physica A:Statistical Mechanics and its Applications, vol. 316, no. 1–4, pp. 87–114, 2002. DOI:  10.1016/S0378-4371(02)01383-3.
[13] J. Barunik, T. Aste, T. Di Matteo, R. P. Liu. Understanding the source of multifractality in financial markets. Physica A:Statistical Mechanics and its Applications, vol. 391, no. 17, pp. 4234–4251, 2012. DOI:  10.1016/j.physa.2012.03.037.
[14] B. Mandelbrot, R. L. Hudson. The Misbehavior of Markets: A Fractal View of Financial Turbulence, New York, USA: Basic Books, 2005.
[15] H. P. Kriegel, P. Kröger, A. Zimek. Outlier detection techniques. In Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, USA, 2010.
[16] K. G. Mehrotra, C. K. Mohan, H. M. Huang. Anomaly Detection Principles and Algorithms, Cham, Germany: Springer, 2017. DOI: 10.1007/978-3-319-67526-8.
[17] B. Peirce. Criterion for the rejection of doubtful observations. Astronomical Journal, vol. 2, no. 45, pp. 161–163, 1852. DOI:  10.1086/100259.
[18] J. Irwin. On a criterion for the rejection of outlying observations. Biometrika, vol. 17, no. 3–4, pp. 238–250, 1925. DOI:  10.1093/biomet/17.3-4.238.
[19] E. S. Pearson, C. C. Sekar. The efficiency of statistical tools and a criterion for the rejection of outlying observations. Biometrika, vol. 28, no. 3–4, pp. 308–320, 1936. DOI:  10.1093/biomet/28.3-4.308.
[20] F. E. Grubbs. Sample criteria for testing outlying observations. The Annals of Mathematical Statistics, vol. 21, no. 1, pp. 27–58, 1950. DOI:  10.1214/aoms/1177729885.
[21] N. A. Heckert, J. J. Filliben, C. M. Croarkin, B. Hembree, W. F. Guthrie, P. Tobias, J. Prinz. NIST/SEMATECH e-Handbook of Statistical Methods, 2012, [Online], Available: http://www.itl.nist.gov/div898/handbook/, February 08, 2020.
[22] F. Rosado. Outliers: The strength of minors. New Advances in Statistical Modeling and Applications, A. Pacheco, R. Santos, M. D. R. Oliveira, C. D. Paulino, Eds., Cham, Germany: Springer, 2014.
[23] D. L. Whaley III. The Interquartile Range: Theory and Estimation, Master dissertation, Faculty of the Department of Mathematics, East Tennessee State University, USA, 2005.
[24] G. L. Tietjen, R. H. Moore. Some grubbs-type statistics for the detection of several outliers. Technometrics, vol. 14, no. 3, pp. 583–597, 1972. DOI:  10.1080/00401706.1972.10488948.
[25] M. Hubert, M. Debruyne. Minimum covariance determinant. WIREs Computational Statistics, vol. 2, no. 1, pp. 36–43, 2010. DOI:  10.1002/wics.61.
[26] B. Rosner. Percentage points for a generalized ESD many-outlier procedure. Technometrics, vol. 25, no. 2, pp. 165–172, 1983. DOI:  10.1080/00401706.1983.10487848.
[27] R. Thompson. A note on restricted maximum likelihood estimation with an alternative outlier model. Journal of the Royal Statistical Society:Series B (Methodological), vol. 47, no. 1, pp. 53–55, 1985. DOI:  10.1111/j.2517-6161.1985.tb01329.x.
[28] P. J. Huber Hoboker, E. M. Ronchetti. Robust Statistics, 2nd ed., Hoboken, USA: Wiley, 2009. DOI: 10.1002/9780470434697.
[29] R. K. Pearson. Mining Imperfect Data: Dealing with Contamination and Incomplete Records, Philadelphia, USA: SIAM, 2005.
[30] N. N. Taleb. Real-world Statistical Consequences of Fat Tails: Papers and Commentary, UK:STEM Academic Press, 2018.
[31] P. D. Domański. Statistical measures. Control Performance Assessment: Theoretical Analyses and Industrial Practice, P. D. Domański, Ed., Cham, Germany: Springer, pp. 53–74, 2020. DOI: 10.1007/978-3-030-23593-2_4.
[32] P. D. Domański. Non-Gaussian properties of the real industrial control error in SISO loops. In Proceedings of the 19th International Conference on System Theory, Control and Computing, IEEE, Cheile Gradistei, Romania, pp. 877–882, 2015. DOI: 10.1109/ICSTCC.2015.7321405.
[33] K. Malik, H. Sadawarti, G. S. Kalra. Comparative analysis of outlier detection techniques. International Journal of Computer Applications, vol. 97, no. 8, pp. 12–21, 2014. DOI:  10.5120/17026-7318.
[34] S. A. Shaikh, H. Kitagawa. Top-k outlier detection from uncertain data. International Journal of Automation and Computing, vol. 11, no. 2, pp. 128–142, 2014. DOI:  10.1007/s11633-014-0775-8.
[35] Z. G. Ding, D. J. Du, M. R. Fei. An isolation principle based distributed anomaly detection method in wireless sensor networks. International Journal of Automation and Computing, vol. 12, no. 4, pp. 402–412, 2015. DOI:  10.1007/s11633-014-0847-9.
[36] S. Banerjee, T. Chattopadhyay, U. Garain. A wide learning approach for interpretable feature recommendation for 1-d sensor data in iot analytics. International Journal of Automation and Computing, vol. 16, no. 6, pp. 800–811, 2019. DOI:  10.1007/s11633-019-1185-8.
[37] N. N. R. Ranga Suri, N. Murty M, G. Athithan. Outlier Detection: Techniques and Applications: A Data Mining Perspective, Cham, Germany: Springer, 2019. DOI: 10.1007/978-3-030-05127-3.
[38] A. Zimek, P. Filzmoser. There and back again: Outlier detection between statistical reasoning and data mining algorithms. WIRES Data Mining and Knowledge Discovery, vol. 8, no. 6, Article number e1280, 2018. DOI:  10.1002/widm.1280.
[39] P. J. Rousseeuw, M. Hubert. Anomaly detection by robust statistics. WIREs Data Mining and Knowledge Discovery, vol. 8, no. 2, Article number e1236, 2018. DOI:  10.1002/widm.1236.
[40] M. Templ, J. Gussenbauer, P. Filzmoser. Evaluation of robust outlier detection methods for zero-inflated complex data. Journal of Applied Statistics, vol. 47, no. 7, pp. 1144–1167, 2020. DOI:  10.1080/02664763.2019.1671961.
[41] M. P. J. Van Der Loo. Distribution based Outlier Detection in Univariate Data. Technical Report Discussion Paper 10003, Statistics Netherlands, The Hague/Heerlen, Netherlands, 2010.
[42] G. Barbato, E. M. Barini, G. Genta, R. Levi. Features and performance of some outlier detection methods. Journal of Applied Statistics, vol. 38, no. 10, pp. 2133–2149, 2011. DOI:  10.1080/02664763.2010.545119.
[43] M. Gupta, J. Gao, C. Aggarwal, J. W. Han. Outlier Detection for Temporal Data, San Rafael, USA: Morgan & Claypool Publishers, 2014. DOI: 10.2200/S00573ED1V01Y201403DMK008.
[44] P. D. Domański. Statistical measures for proportional–integral–derivative control quality: Simulations and industrial data. Proceedings of the Institution of Mechanical Engineers,Part I:Journal of Systems and Control Engineering, vol. 232, no. 4, pp. 428–441, 2018. DOI:  10.1177/0959651817754034.
[45] P. D. Domański, S. Golonka, P. M. Marusak, B. Moszowski. Robust and asymmetric assessment of the benefits from improved control – industrial validation. IFAC-PapersOnLine, vol. 51, no. 18, pp. 815–820, 2018. DOI:  10.1016/j.ifacol.2018.09.260.
[46] L. B. Klebanov. Big outliers versus heavy tails: What to use? https://arxiv.org/abs/1611.05410.
[47] C. Croux, C. Dehon. Robust estimation of location and scale. Encyclopedia of Environmetrics, A. H. El-Shaarawi, W. W. Piegorsch, Eds., Hoboken, USA: Wiley, 2013. DOI: 10.1002/9780470057339.vnn093.
[48] S. Verboven, M. Hubert. LIBRA: A MATLAB library for robust analysis. Chemometrics and Intelligent Laboratory Systems, vol. 75, no. 2, pp. 127–136, 2005. DOI:  10.1016/j.chemolab.2004.06.003.
[49] J. H. McCulloch. Simple consistent estimators of stable distribution parameters. Communications in Statistics − Simulation and Computation, vol. 15, no. 4, pp. 1109–1136, 1986. DOI:  10.1080/03610918608812563.
[50] I. A. Koutrouvelis. Regression-type estimation of the parameters of stable laws. Journal of the American Statistical Association, vol. 75, no. 372, pp. 918–928, 1980. DOI:  10.1080/01621459.1980.10477573.
[51] E. E. Kuruoglu. Density parameter estimation of skewed α-stable distributions. IEEE Transactions on Signal Processing, vol. 49, no. 10, pp. 2192–2201, 2001. DOI:  10.1109/78.950775.
[52] S. Borak, A. Misiorek, R. Weron. Models for heavy-tailed asset returns. Statistical Tools for Finance and Insurance, 2nd ed., P. Cizek, W. K. Härdle, R. Weron, Eds., Berlin, Heidelberg, Germany: Springer, pp. 21–55, 2011. DOI: 10.1007/978-3-642-18062-0_1.
[53] A. Alfons, M. Templ, P. Filzmoser. Robust estimation of economic indicators from survey samples based on pareto tail modelling. Journal of the Royal Statistical Society:Series C, vol. 62, no. 2, pp. 271–286, 2013. DOI:  10.1111/j.1467-9876.2012.01063.x.
[54] J. Danielsson, L. M. Ergun, L. De Haan, C. G. De Vries. Tail Index Estimation: Quantile Driven Threshold Selection, Bank of Canada Staff Working Paper 2019–28, Bank of Canada.
[55] P. D. Domański. Control Performance Assessment: Theoretical Analyses and Industrial Practice, Cham, Germany: Springer, 2020. DOI: 10.1007/978-3-030-23593-2.
[56] M. C. Bryson. Heavy-tailed distributions: Properties and tests. Technometrics, vol. 16, no. 1, pp. 61–68, 1974. DOI:  10.1080/00401706.1974.10489150.
[57] L. B. Klebanov, I. Volchenkova. Outliers and the ostensibly heavy tails. https://arxiv.org.abs/1807.08715vl.
[58] G. Marsaglia, W. W. Tsang. A simple method for generating gamma variables. ACM Transactions on Mathematical Software, vol. 26, no. 3, pp. 363–372, 2000. DOI:  10.1145/358407.358414.
[59] N. L. Johnson, S. Kotz, N. Balakrishnan. Continuous Univariate Distributions, 2nd ed., New York, USA: Wiley, 1995.