References
- Aggarwal, C. C. (2017). Outlier Analysis (2nd ed.). Springer Publishing Company. https://doi.org/10.1007/978-3-319-47578-310.1007/978-3-319-47578-3
- Angiulli, F., & Pizzuti, C. (2002). Fast Outlier Detection in High Dimensional Spaces, In: Elomaa T., Mannila H., Toivonen H. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2002. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence), vol. 2431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45681-3_210.1007/3-540-45681-3_2
- Baldominos, A., Blanco, I., Moreno, A. J., Iturrarte, R., Bernárdez, Ó., & Afonso, C. (2018). Identifying Real Estate Opportunities Using Machine Learning. Applied Sciences (Basel, Switzerland), 8(11), 2321. https://doi.org/10.3390/app811232110.3390/app8112321
- Batóg, B., & Foryś, I. (2011). Logit models in the analysis of transactions on the Warsaw residential market in Polish: Modele logitowe w analizie transakcji na warszawskim rynku mieszkaniowym. Studia i Materiały Towarzystwa Naukowego Nieruchomości, 19(3), 33–48.
- Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000). LOF: Identifying density-based local outliers. SIGMOD Record, 29(2), 93–104. https://doi.org/10.1145/335191.33538810.1145/335191.335388
- Cook, R. D. (1977). Detection of Influential Observation in Linear Regression. Technometrics, 19(1), 15–18.
- Domingues, R., Filippone, M., Michiardi, P., & Zouaoui, J. (2018). A comparative evaluation of outlier detection algorithms: Experiments and analyses. Pattern Recognition, 74, 406–421. https://doi.org/10.1016/j.patcog.2017.09.03710.1016/j.patcog.2017.09.037
- Doszyń, M. (Ed.). (2020). Attribute influence matrix calibration system in Szczecin’s algorithm of mass property valuation. University of Szczecin.
- Doszyń, M., Gnat, S., & Bas, M. (2017). The Econometric Procedures of Specific Transactions Identification. Folia Oeconomica Stetinensia, 17(1), 20–30. https://doi.org/10.1515/foli-2017-000210.1515/foli-2017-0002
- Etel, L., & Dowgier, R. (2013). Local taxes and charges – time for a change in Polish: Podatki i opłaty lokalne – czas na zmiany, Białystok. Temida : Casopis o Viktimizaciji, Ljudskim Pravima i Rodu, 2.
- Głuszak, M., & Marona, B. (2015). Cadastral tax. Economic conditions of the property taxation reform in Polish Podatek katastralny. Ekonomiczne uwarunkowania reformy opodatkowania nieruchomości. Poltext.
- Gnat, S. (2009). Analysis of the effects of replacing current property tax with ad valorem property tax in a sample municipality. Folia Oeconomica Stetinensia, 8(16), 82-98.10.2478/v10031-009-0022-6
- Gnat, S. (2010). Use of operational research methods in modelling the impact of cadastral tax on the financial situation of the municipality in Polish: Wykorzystanie metod badań operacyjnych w modelowaniu wpływu podatku katastralnego na sytuację finansową gminy. Doctoral Dissertation, Univesrity of Szczecin, Szczecin.
- Gnat, S. (2018). Analysis of Communes’ Potential Fall in Revenue Following Introduction of ad Valorem Property Tax. Real Estate Management and Valuation, 26(1), 63–72. https://doi.org/10.2478/remav-2018-000610.2478/remav-2018-0006
- Grubbs, F. E. (1969). Procedures for Detecting Outlying Observations in Samples. Technometrics, 11(1), 1–21. https://doi.org/10.1080/00401706.1969.1049065710.1080/00401706.1969.10490657
- Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning. Springer. https://doi.org/10.1007/978-0-387-84858-710.1007/978-0-387-84858-7
- Hawkins, D. (1980). Identification of Outliers. Chapman and Hall. https://doi.org/10.1007/978-94-015-3994-410.1007/978-94-015-3994-4
- He, Z., Xu, X., & Deng, S. (2003). Discovering cluster-based local outliers. Pattern Recognition Letters, 24(9-10), 1641–1650. https://doi.org/10.1016/S0167-8655(03)00003-510.1016/S0167-8655(03)00003-5
- Johnson, R. (1992). Applied Multivariate Statistical Analysis. Prentice Hall.
- Jolliffe, I. (2002). Principal Component Analysis (2nd ed.). Springer.
- Kontrimas, V., & Verikas, A. (2006). Tracking of doubtful real estate transaction by outlier detection methods: A comparative study. Information Technology and Control, 35(2), 94–105.
- Liu, F. T., Ting, K. M., & Zhou, Z. 2008, Isolation Forest, Eighth IEEE International Conference on Data Mining, Pisa, 2008, 413-422.
- Liu, H., Shah, S., & Jiang, W. (2004). On-line outlier detection and data cleaning. Computers & Chemical Engineering, 28(9), 1635–1647. https://doi.org/10.1016/j.compchemeng.2004.01.00910.1016/j.compchemeng.2004.01.009
- Maimon, O. (ed.), & Rokach, L. (ed.). (2005). Data Mining and Knowledge Discovery Handbook. Springer-Verlag. https://doi.org/10.1007/b10740810.1007/b107408
- Morano, P., De Mare, G., & Tajani, F. (2013). LMS for Outliers Detection in the Analysis of a Real Estate Segment of Bari. In B. Murgante, . . . (Eds.), Lecture Notes in Computer Science: Vol. 7974. Computational Science and Its Applications – ICCSA 2013. ICCSA 2013. Springer. https://doi.org/10.1007/978-3-642-39649-6_3310.1007/978-3-642-39649-6_33
- Ng, K. H., & Khor, K. (2016). Rapid identification of outstanding real estate investment trusts with outlier detection algorithms. Journal of Theoretical and Applied Information Technology, 88(2), 321–330.
- Pimentel, M. A. F., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014). A review of novelty detection. Signal Processing, 99, 215–249. https://doi.org/10.1016/j.sigpro.2013.12.02610.1016/j.sigpro.2013.12.026
- Prastawa, M., Bullitt, E., Ho, S., & Gerig, G. (2004). A brain tumor segmentation framework based on outlier detection. Medical Image Analysis, 8(3), 275–283. https://doi.org/10.1016/j.media.2004.06.007 PMID:1545022210.1016/j.media.2004.06.007
- Raschka, S. (2018). MLxtend: Providing machine learning and data science utilities and extensions to Python’s scientific computing stack. Journal of Open Source Software, 3(24), 638. https://doi.org/10.21105/joss.0063810.21105/joss.00638
- Sammut, C. (ed.), & Webb, G. I. (ed.). (2017). Encyclopedia of Machine Learning and Data Mining (2nd ed.). Springer Publishing Company. https://doi.org/10.1007/978-1-4899-7687-110.1007/978-1-4899-7687-1
- Stefansky, W. (1972). Rejecting Outliers in Factorial Designs. Technometrics, 14(2), 469–479. https://doi.org/10.1080/00401706.1972.1048893010.1080/00401706.1972.10488930
- Śpiewak, B. (2018). Application of Chosen Methods of Robust Estimation: Baarda’s and Huber’s in Search for Outliers in the Real Estate Market Modeling. Folia Oeconomica Stetinensia, 18(1), 27–38. https://doi.org/10.2478/foli-2018-000310.2478/foli-2018-0003
- Trojanek, M., & Kisiała, W. (2016). The Diversification of Communes’ Revenue from Real Estate Across Provinces. Real Estate Management and Valuation, 24(2), 36–49. https://doi.org/10.1515/remav-2016-001210.1515/remav-2016-0012
- Wójtowicz, K. (2006). Analysis of potential effects of real estate tax system reform in Poland in Polish: Analiza potencjalnych skutków reformy systemu opodatkowania nieruchomości w Polsce. Finanse Publiczne. UMCS.
- Worden, K., Manson, G., & Fieller, N. R. J. (2000). Damage detection using outlier analysis. Journal of Sound and Vibration, 229(3), 647–667. https://doi.org/10.1006/jsvi.1999.251410.1006/jsvi.1999.2514
- Zhao, Y., Nasrullah, Z., & Li, Z. (2019). PyOD: A Python Toolbox for Scalable Outlier Detection. Journal of Machine Learning Research, 20(96), 1–7.
- Zhu, C., Kitagawa, H., Papadimitriou, S., & Faloutsos, C. (2011). Outlier detection by example. Journal of Intelligent Information Systems, 36, 217–247. https://doi.org/10.1007/s10844-010-0128-110.1007/s10844-010-0128-1
- Zyga, J. (2016). Connection Between Similarity and Estimation Results of Property Values Obtained by Statistical Methods. Real Estate Management and Valuation, 24(3), 5–15. https://doi.org/10.1515/remav-2016-001710.1515/remav-2016-0017