This work is licensed under the Creative Commons Attribution 4.0 International License.
Agyemang, M., Barker, K., & Alhajj, R. (2006). A comprehensive survey of numeric and symbolic outlier mining techniques. Intelligent Data Analysis, 10(6), 521–538. https://doi.org/10.3233/IDA-2006-10604AgyemangM.BarkerK.AlhajjR.2006A comprehensive survey of numeric and symbolic outlier mining techniques106521538https://doi.org/10.3233/IDA-2006-1060410.3233/IDA-2006-10604Search in Google Scholar
Amer, M., & Goldstein, M. (2012). Nearest-Neighbor and Clustering based Anomaly Detection Algorithms for RapidMiner. In 3rd RapidMiner Community Meeting and Conferernce. https://doi.org/10.5455/ijavms.141AmerM.GoldsteinM.2012In3rd RapidMiner Community Meeting and Confererncehttps://doi.org/10.5455/ijavms.14110.5455/ijavms.141Search in Google Scholar
Bailey, J. J., & O’Connor, R. J. (1975). Operationalizing Incrementalism: Measuring the Muddles. Public Administration Review, 35(1), 60–66. https://doi.org/10.2307/975202BaileyJ. J.O’ConnorR. J.1975Operationalizing Incrementalism: Measuring the Muddles3516066https://doi.org/10.2307/97520210.2307/975202Search in Google Scholar
Barnett, V., & Lewis, T. (1994). Outliers in Statistical Data (3rd Ed.). Wiley.BarnettV.LewisT.19943rd Ed.WileySearch in Google Scholar
Bartolucci, A. A. (2016). Methodologies in Outlier Analysis. In A. A. Bartolucci, K. P. Singh, & S. Bae (Eds.), Introduction to Statistical Analysis of Laboratory Data (pp. 79–111). John Wiley & Sons.BartolucciA. A.2016Methodologies in Outlier AnalysisInBartolucciA. A.SinghK. P.BaeS.(Eds.),79111John Wiley & Sons10.1002/9781118736890.ch4Search in Google Scholar
Baumgartner, F. R., & Epp, D. A. (2013). Explaining Punctuations (Paper presented at the annual meetings of the Comparative Agendas Project, Antwerp, Belgium, June 27–29, 2013). Retrieved from https://unc.live/3afbnxBBaumgartnerF. R.EppD. A.2013Paper presented at the annual meetings of the Comparative Agendas ProjectAntwerp, BelgiumJune 27–29, 2013Retrieved from https://unc.live/3afbnxBSearch in Google Scholar
Baumgartner, F. R., Green-Pedersen, C., & Jones, B. D. (2006). Comparative studies of policy agendas. Journal of European Public Policy, 13(7), 959–974.BaumgartnerF. R.Green-PedersenC.JonesB. D.2006Comparative studies of policy agendas13795997410.4324/9781315878522Search in Google Scholar
Baumgartner, F. R., & Jones, B. D. (Eds.). (2002). Policy dynamics. Chicago: University of Chicago Press.BaumgartnerF. R.JonesB. D.(Eds.).2002ChicagoUniversity of Chicago PressSearch in Google Scholar
Bozeman, B. (1977). The Effect of Economic and Partisan Change On Federal Appropriations. Western Political Quarterly, 30(1), 112–124. https://doi.org/10.1177/106591297703000111BozemanB.1977The Effect of Economic and Partisan Change On Federal Appropriations301112124https://doi.org/10.1177/10659129770300011110.1177/106591297703000111Search in Google Scholar
Breunig, C. (2006). The more things change, the more things stay the same: a comparative analysis of budget punctuations. Journal of European Public Policy, 13(7), 1069–1085. https://doi.org/10.1080/13501760600924167BreunigC.2006The more things change, the more things stay the same: a comparative analysis of budget punctuations13710691085https://doi.org/10.1080/1350176060092416710.1080/13501760600924167Search in Google Scholar
Breunig, C., & Jones, B. D. (2011). Stochastic Process Methods with an Application to Budgetary Data. Political Analysis, 19(1), 103–17.BreunigC.JonesB. D.2011Stochastic Process Methods with an Application to Budgetary Data1911031710.1093/pan/mpq038Search in Google Scholar
Breunig, C., & Koski, C. (2006). Punctuated Equilibria and Budgets in the American States. Policy Studies Journal, 34(3), 363–379. https://doi.org/10.1111/j.1541-0072.2006.00177.xBreunigC.KoskiC.2006Punctuated Equilibria and Budgets in the American States343363379https://doi.org/10.1111/j.1541-0072.2006.00177.x10.1111/j.1541-0072.2006.00177.xSearch in Google Scholar
Breunig, M., Kriegel, H.-P., Ng, R., & Sander, J. (2000). LOF: Identifying Density-Based Local Outliers. In ACM Sigmod Record (Vol. 29, pp. 93–104). Dallas, TX. https://doi.org/10.1145/342009.335388BreunigM.KriegelH.-P.NgR.SanderJ.2000InACM Sigmod Record2993104Dallas, TXhttps://doi.org/10.1145/342009.33538810.1145/342009.335388Search in Google Scholar
Bunce, V., & Echols, J. M. (1978). Power and Policy in Communist Systems: The Problem of ‘Incrementalism’. The Journal of Politics, 40(4), 911–932. https://doi.org/10.2307/2129902BunceV.EcholsJ. M.1978Power and Policy in Communist Systems: The Problem of ‘Incrementalism’404911932https://doi.org/10.2307/212990210.2307/2129902Search in Google Scholar
Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly Detection: A Survey. ACM Computing Surveys, 41(3), 1–58. https://doi.org/10.1145/1541880.1541882ChandolaV.BanerjeeA.KumarV.2009Anomaly Detection: A Survey413158https://doi.org/10.1145/1541880.154188210.1145/1541880.1541882Search in Google Scholar
Cox, J., Hager, G., & Lowery, D. (1993). Regime Change in Presidential and Congressional Budgeting: Role Discontinuity or Role Evolution? American Journal of Political Science, 37(1), 88–118. https://doi.org/10.2307/2111525CoxJ.HagerG.LoweryD.1993Regime Change in Presidential and Congressional Budgeting: Role Discontinuity or Role Evolution?37188118https://doi.org/10.2307/211152510.2307/2111525Search in Google Scholar
Davis, O. A., Dempster, M. A. H., & Wildavsky, A. (1974). Towards A Predictive Theory of Government Expenditure: US Domestic Appropriations. British Journal of Political Science, 4(4), 419–452. https://doi.org/DOI:10.1017/S0007123400009650DavisO. A.DempsterM. A. H.WildavskyA.1974Towards A Predictive Theory of Government Expenditure: US Domestic Appropriations44419452https://doi.org/DOI:10.1017/S000712340000965010.1017/S0007123400009650Search in Google Scholar
de Crombrugghe, A., & Lipton, D. (1994). The Government Budget and the Economic Transformation of Poland. In O. Blanchard, K. Froot, & J. Sachs (Eds.), Transition in Eastern Europe (Vol. 2, pp. 111–136). Chicago, Illinois: University of Chicago Press.de CrombruggheA.LiptonD.1994The Government Budget and the Economic Transformation of PolandInBlanchardO.FrootK.SachsJ.(Eds.),2111136Chicago, IllinoisUniversity of Chicago PressSearch in Google Scholar
Dempster, M., & Wildavsky, A. (1979). On Change: Or, There is No Magic Size for An Increment. Political Studies, 27(3), 371–389. https://doi.org/10.1111/j.1467-9248.1979.tb01210.xDempsterM.WildavskyA.1979On Change: Or, There is No Magic Size for An Increment273371389https://doi.org/10.1111/j.1467-9248.1979.tb01210.x10.1111/j.1467-9248.1979.tb01210.xSearch in Google Scholar
Denning, D. (1986). An Intrusion-Detection Model. In IEEE Transactions on Software Engineering (Vol. 13, pp. 118–133). IEEE. https://doi.org/10.1109/SP.1986.10010DenningD.1986InIEEE Transactions on Software Engineering13118133IEEEhttps://doi.org/10.1109/SP.1986.1001010.1109/SP.1986.10010Search in Google Scholar
Dezhbakhsh, H., Tohamy, S. M., & Aranson, P. H. (2003). A new approach for testing budgetary incrementalism. Journal of Politics, 65(2), 532–558. https://doi.org/10.1111/1468-2508.t01-3-00014DezhbakhshH.TohamyS. M.AransonP. H.2003A new approach for testing budgetary incrementalism652532558https://doi.org/10.1111/1468-2508.t01-3-0001410.1111/1468-2508.t01-3-00014Search in Google Scholar
Endler, D. (1998). Intrusion detection. Applying machine learning to Solaris audit data. In Proceedings 14th Annual Computer Security Applications Conference (pp. 268–279). Phoenix, AZ. https://doi.org/10.1109/CSAC.1998.738647EndlerD.1998InProceedings 14th Annual Computer Security Applications Conference268279Phoenix, AZhttps://doi.org/10.1109/CSAC.1998.73864710.1109/CSAC.1998.738647Search in Google Scholar
Fenno, R. F. (1966). The Power of the Purse. Boston: Little Brown.FennoR. F.1966BostonLittle BrownSearch in Google Scholar
Flink, C. M. (2017). Rethinking Punctuated Equilibrium Theory: A Public Administration Approach to Budgetary Changes. Policy Studies Journal, 45(1), 101–120. https://doi.org/10.1111/psj.12114FlinkC. M.2017Rethinking Punctuated Equilibrium Theory: A Public Administration Approach to Budgetary Changes451101120https://doi.org/10.1111/psj.1211410.1111/psj.12114Search in Google Scholar
Flink, C. M., & Robinson, S. E. (2020). Corrective policy reactions: Positive and negative budgetary punctuations. Journal of Public Policy, 40(1), 96–115. https://doi.org/10.1017/S0143814X18000259FlinkC. M.RobinsonS. E.2020Corrective policy reactions: Positive and negative budgetary punctuations40196115https://doi.org/10.1017/S0143814X1800025910.1017/S0143814X18000259Search in Google Scholar
Gist, J. R. (1974). Mandatory Expenditures and the Defense Sector: Theory of Budgetary Incrementalism. In R. B. Ripley (Ed.), A Sage Professional Paper (pp. 5–39). London: Sage.GistJ. R.1974Mandatory Expenditures and the Defense Sector: Theory of Budgetary IncrementalismInRipleyR. B.(Ed.),539LondonSageSearch in Google Scholar
Goldstein, M., & Uchida, S. (2016). A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. PLoS ONE, 11(4), 1–31. https://doi.org/10.1371/journal.pone.0152173GoldsteinM.UchidaS.2016A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data114131https://doi.org/10.1371/journal.pone.015217310.1371/journal.pone.0152173Search in Google Scholar
Gould, P. (1981). Letting the Data Speak for Themselves. Annals of the Association of American Geographers, 71(2), 166–176.GouldP.1981Letting the Data Speak for Themselves71216617610.1111/j.1467-8306.1981.tb01346.xSearch in Google Scholar
Grubbs, F. E. (1969). Procedures for Detecting Outlying Observations in Samples. Technometrics, 11(1), 1–21. https://doi.org/10.1080/00401706.1969.10490657GrubbsF. E.1969Procedures for Detecting Outlying Observations in Samples111121https://doi.org/10.1080/00401706.1969.1049065710.1080/00401706.1969.10490657Search in Google Scholar
Hampel, F. R. (1971). A General Qualitative Definition of Robustness. The Annals of Mathematical Statistics, 42(6), 1887–1896.HampelF. R.1971A General Qualitative Definition of Robustness4261887189610.1214/aoms/1177693054Search in Google Scholar
Hawkins, D. M. (1980). Identification of outliers. London: Chapman and Hall. https://doi.org/10.1007/978-94-015-3994-4HawkinsD. M.1980LondonChapman and Hallhttps://doi.org/10.1007/978-94-015-3994-410.1007/978-94-015-3994-4Search in Google Scholar
Helman, P., & Bhangoo, J. (1997). A Statistically Based System for Prioritizing Information Exploration under Uncertainty. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (Vol. 27, pp. 449–466). IEEE Press. https://doi.org/10.1109/3468.594912HelmanP.BhangooJ.1997InProceedings of the IEEE International Conference on Systems, Man, and Cybernetics27449466IEEE Presshttps://doi.org/10.1109/3468.59491210.1109/3468.594912Search in Google Scholar
Hodge, V., & Austin, J. (2004). A Survey of Outlier Detection Methodologies. Artificial Intelligence Review, 22(2), 85–126. https://doi.org/10.1023/B:AIRE.0000045502.10941.a9HodgeV.AustinJ.2004A Survey of Outlier Detection Methodologies22285126https://doi.org/10.1023/B:AIRE.0000045502.10941.a910.1023/B:AIRE.0000045502.10941.a9Search in Google Scholar
Jones, B. D., Baumgartner, F. R., Breunig, C., Wlezien, C., Soroka, S., Foucault, M., … John, P. (2009). A General Empirical Law of Public Budgets: A Comparative Analysis. American Journal of Political Science, 53(4), 855–73.JonesB. D.BaumgartnerF. R.BreunigC.WlezienC.SorokaS.FoucaultM.JohnP.2009A General Empirical Law of Public Budgets: A Comparative Analysis5348557310.1111/j.1540-5907.2009.00405.xSearch in Google Scholar
Jones, B. D., Baumgartner, F. R., & True, J. L. (1998). Policy Punctuations: US Budget Authority, 1947–1995. Journal of Poliitics, 60(1), 1–33.JonesB. D.BaumgartnerF. R.TrueJ. L.1998Policy Punctuations: US Budget Authority, 1947–199560113310.2307/2647999Search in Google Scholar
Jones, B. D., Sulkin, T., & Larsen, H. A. (2003). Policy Punctuations in American Political Institutions. American Political Science Review, 97(1), 151–169.JonesB. D.SulkinT.LarsenH. A.2003Policy Punctuations in American Political Institutions97115116910.1017/S0003055403000583Search in Google Scholar
Jones, B. D., True, J. L., & Baumgartner, F. R. (1997). Does Incrementalism Stem from Political Consensus or from Institutional Gridlock? American Journal of Political Science, 41(4), 1319–1339.JonesB. D.TrueJ. L.BaumgartnerF. R.1997Does Incrementalism Stem from Political Consensus or from Institutional Gridlock?4141319133910.2307/2960491Search in Google Scholar
Jordan, A. A., Taylor, W. J., Meese, M. J., Nielsen, S. C., & Schlesinger, J. (2009). American National Security (6th ed.). Baltimore: Johns Hopkins University Press.JordanA. A.TaylorW. J.MeeseM. J.NielsenS. C.SchlesingerJ.20096th ed.BaltimoreJohns Hopkins University Press10.1353/book.26472Search in Google Scholar
Jordan, M. M. (2003). Punctuations and Agendas: A New Look at Local Government Budget Expenditures. Journal of Policy Analysis and Management, 22(3), 345–360. https://doi.org/10.1002/pam.10136JordanM. M.2003Punctuations and Agendas: A New Look at Local Government Budget Expenditures223345360https://doi.org/10.1002/pam.1013610.1002/pam.10136Search in Google Scholar
Kamlet, M. S., & Mowery, D. C. (1987). Influences on Executive and Congressional Budgetary Priorities, 1955–1981. American Political Science Review, 81(1), 155–178. https://doi.org/DOI:10.2307/1960783KamletM. S.MoweryD. C.1987Influences on Executive and Congressional Budgetary Priorities, 1955–1981811155178https://doi.org/DOI:10.2307/196078310.2307/1960783Search in Google Scholar
Kanter, A. (1972). Congress and the Defense Budget: 1960–1970. American Political Science Review, 66(1), 129–143.KanterA.1972Congress and the Defense Budget: 1960–197066112914310.2307/1959282Search in Google Scholar
Kemp, K. (1982). Instability in budgeting for federal regulatory agencies. Social Science Quarterly, 63(4), 643–660.KempK.1982Instability in budgeting for federal regulatory agencies634643660Search in Google Scholar
Khan, S. S., & Madden, M. G. (2014). One-class classification: Taxonomy of study and review of techniques. The Knowledge Engineering Review, 29(3), 345–374. https://doi.org/10.1017/S026988891300043XKhanS. S.MaddenM. G.2014One-class classification: Taxonomy of study and review of techniques293345374https://doi.org/10.1017/S026988891300043X10.1017/S026988891300043XSearch in Google Scholar
Knorr, E. M., & Ng, R. T. (1998). Algorithms for Mining Distance-Based Outliers in Large Datasets. In Proceedings of the 24rd International Conference on Very Large Data Bases (pp. 392–403). San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.KnorrE. M.NgR. T.1998InProceedings of the 24rd International Conference on Very Large Data Bases392403San Francisco, CA, USAMorgan Kaufmann Publishers IncSearch in Google Scholar
Knorr, E. M., Ng, R. T., & Zamar, R. (2001). Robust Space Transformations for Distance-based Operations. In Proceedings of the 7th International Conference on Knowledge Discovery and Data Mining (KDD01) (pp. 126–135). https://doi.org/10.1145/502512.502532KnorrE. M.NgR. T.ZamarR.2001InProceedings of the 7th International Conference on Knowledge Discovery and Data Mining (KDD01)126135https://doi.org/10.1145/502512.50253210.1145/502512.502532Search in Google Scholar
Laptev, N., Amizadeh, S., & Flint, I. (2015). Generic and Scalable Framework for Automated Time-Series Anomaly Detection. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1939–1947). New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/2783258.2788611LaptevN.AmizadehS.FlintI.2015InProceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining19391947New York, NY, USAAssociation for Computing Machineryhttps://doi.org/10.1145/2783258.278861110.1145/2783258.2788611Search in Google Scholar
Laurikkala, J., Juhola, M., & Kentala, E. (2000). Informal identification of outliers in medical data. In N. Lavrač, S. Miksch, & B. Kavšek (Eds.), Fifth International Workshop on Intelligent Data Analysis in Medicine and Pharmacology IDAMAP-2000 Berlin, 22 August. Organized as a workshop of the 14th European Conference on Artificial Intelligence ECAI-2000. https://doi.org/10.1142/s0217979205027834LaurikkalaJ.JuholaM.KentalaE.2000InLavračN.MikschS.KavšekB.(Eds.),Fifth International Workshop on Intelligent Data Analysis in Medicine and Pharmacology IDAMAP-2000 Berlin, 22 August. Organized as a workshop of the 14th European Conference on Artificial Intelligence ECAI-2000https://doi.org/10.1142/s021797920502783410.1142/S0217979205027834Search in Google Scholar
Leys, C., Ley, C., Klein, O., Bernard, P., & Licata, L. (2013). Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology, 49(4), 764–766. https://doi.org/10.1016/j.jesp.2013.03.013LeysC.LeyC.KleinO.BernardP.LicataL.2013Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median494764766https://doi.org/10.1016/j.jesp.2013.03.01310.1016/j.jesp.2013.03.013Search in Google Scholar
Lindblom, C. E. (1959). The Science of ‘Muddling Through’. Public Administration Review, 19(2), 79–88. https://doi.org/10.2307/973677LindblomC. E.1959The Science of ‘Muddling Through’1927988https://doi.org/10.2307/97367710.2307/973677Search in Google Scholar
Merriam-Webster Online Dictionary. (2019). Retrieved 14 November 2019, from https://www.merriam-webster.com/dictionary2019Retrieved 14 November 2019, from https://www.merriam-webster.com/dictionarySearch in Google Scholar
Miller, J. (1991). Short Report: Reaction Time Analysis with Outlier Exclusion: Bias Varies with Sample Size. The Quarterly Journal of Experimental Psychology Section A, 43(4), 907–912. https://doi.org/10.1080/14640749108400962MillerJ.1991Short Report: Reaction Time Analysis with Outlier Exclusion: Bias Varies with Sample Size434907912https://doi.org/10.1080/1464074910840096210.1080/14640749108400962Search in Google Scholar
Mueller, D. C. (2003). Public Choice III (3rd ed.). Cambridge: Cambridge University Press. https://doi.org/DOI:10.1017/CBO9780511813771MuellerD. C.20033rd ed.CambridgeCambridge University Presshttps://doi.org/DOI:10.1017/CBO978051181377110.1017/CBO9780511813771Search in Google Scholar
Munir, M., Siddiqui, S. A., Dengel, A., & Ahmed, S. (2019). DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series. IEEE Access, 7, 1991–2005. https://doi.org/10.1109/ACCESS.2018.2886457MunirM.SiddiquiS. A.DengelA.AhmedS.2019DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series719912005https://doi.org/10.1109/ACCESS.2018.288645710.1109/ACCESS.2018.2886457Search in Google Scholar
Noguchi, Y. (1980). A Dynamic Model of Incremental Budgeting. Hitotsubashi Journal of Economics, 20, 11–25.NoguchiY.1980A Dynamic Model of Incremental Budgeting201125Search in Google Scholar
Padgett, J. F. (1980). Bounded Rationality in Budgetary Research. American Political Science Review, 74(2), 354–372. https://doi.org/DOI:10.2307/1960632PadgettJ. F.1980Bounded Rationality in Budgetary Research742354372https://doi.org/DOI:10.2307/196063210.2307/1960632Search in Google Scholar
Papadimitriou, S., Kitagawa, H., Gibbons, P. B., & Faloutsos, C. (2002). LOCI: Fast Outlier Detection Using the Local Correlation Integral (No. CMU-CS-02-188). Pittsburgh, PA. https://doi.org/10.1184/R1/6607028.v1PapadimitriouS.KitagawaH.GibbonsP. B.FaloutsosC.2002Pittsburgh, PAhttps://doi.org/10.1184/R1/6607028.v1Search in Google Scholar
Ramaswamy, S., Rastogi, R., & Shim, K. (2000). Efficient Algorithms for Mining Outliers from Large Data Sets. In Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data (pp. 427–438). New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/342009.335437RamaswamyS.RastogiR.ShimK.2000InProceedings of the 2000 ACM SIGMOD International Conference on Management of Data427438New York, NY, USAAssociation for Computing Machineryhttps://doi.org/10.1145/342009.33543710.1145/342009.335437Search in Google Scholar
Robinson, S. E., Caver, F., Meier, K. J., & O’Toole, L. J. (2007). Explaining policy punctuations: Bureaucratization and budget change. American Journal of Political Science, 51(1), 140–150. https://doi.org/10.1111/j.1540-5907.2007.00242.xRobinsonS. E.CaverF.MeierK. J.O’TooleL. J.2007Explaining policy punctuations: Bureaucratization and budget change511140150https://doi.org/10.1111/j.1540-5907.2007.00242.x10.1111/j.1540-5907.2007.00242.xSearch in Google Scholar
Robinson, S. E., Flink, C. M., & King, C. M. (2014). Organizational History and Budgetary Punctuation. Journal of Public Administration Research and Theory, 24(2), 459–471. https://doi.org/10.1093/jopart/mut035RobinsonS. E.FlinkC. M.KingC. M.2014Organizational History and Budgetary Punctuation242459471https://doi.org/10.1093/jopart/mut03510.1093/jopart/mut035Search in Google Scholar
Rousseeuw, P. J., & Hubert, M. (2018). Anomaly detection by robust statistics. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(2), 1–14. https://doi.org/10.1002/widm.1236RousseeuwP. J.HubertM.2018Anomaly detection by robust statistics82114https://doi.org/10.1002/widm.123610.1002/widm.1236Search in Google Scholar
Schubert, E., Wojdanowski, R., Zimek, A., & Kriegel, H. P. (2012). On evaluation of outlier rankings and outlier scores. In Proceedings of the 12th SIAM International Conference on Data Mining, SDM 2012 (pp. 1047–1058). https://doi.org/10.1137/1.9781611972825.90SchubertE.WojdanowskiR.ZimekA.KriegelH. P.2012InProceedings of the 12th SIAM International Conference on Data Mining, SDM 201210471058https://doi.org/10.1137/1.9781611972825.9010.1137/1.9781611972825.90Search in Google Scholar
Sebők, M., & Berki, T. (2017). Incrementalism and punctuated equilibrium in Hungarian budgeting (1991–2013). Journal of Public Budgeting, Accounting and Financial Management, 29(2), 151–181. https://doi.org/10.1108/jpbafm-29-02-2017-b001SebőkM.BerkiT.2017Incrementalism and punctuated equilibrium in Hungarian budgeting (1991–2013)292151181https://doi.org/10.1108/jpbafm-29-02-2017-b00110.1108/JPBAFM-29-02-2017-B001Search in Google Scholar
Shewhart, W. A. (1923). Economic Control of Quality of Manufactured Product. London: D.van Nostrand Company.ShewhartW. A.1923LondonD.van Nostrand CompanySearch in Google Scholar
Simon, H. A. (1955). A Behavioral Model of Rational Choice. The Quarterly Journal of Economics, 69(1), 99–118. https://doi.org/10.2307/1884852SimonH. A.1955A Behavioral Model of Rational Choice69199118https://doi.org/10.2307/188485210.2307/1884852Search in Google Scholar
Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48.SimsC. A.1980Macroeconomics and Reality48114810.2307/1912017Search in Google Scholar
True, J. L., Jones, B. D., & Baumgartner, F. R. (1999). Punctuated-equilibrium theory: explaining stability and change in American policymaking. In P. A. Sabatier (Ed.), Theories of the policy process. Boulder, CO: Westview Press.TrueJ. L.JonesB. D.BaumgartnerF. R.1999Punctuated-equilibrium theory: explaining stability and change in American policymakingInSabatierP. A.(Ed.),Boulder, COWestview PressSearch in Google Scholar
True, J. L., Jones, B. D., & Baumgartner, F. R. (2007). Punctuated-Equilibrium Theory: Explaining Stability and Change in Public Policymaking. In P. A. Sabatier (Ed.), Theories of the Policy Process (pp. 155–88). Boulder, CO: Westview Press.TrueJ. L.JonesB. D.BaumgartnerF. R.2007Punctuated-Equilibrium Theory: Explaining Stability and Change in Public PolicymakingInSabatierP. A.(Ed.),15588Boulder, COWestview PressSearch in Google Scholar
Tukey, J. (1977). Exploratory Data Analysis. Reading, MA: Addison-Wesley.TukeyJ.1977Reading, MAAddison-WesleySearch in Google Scholar
Wanat, J. (1974). Bases of Budgetary Incrementalism. American Political Science Review, 68(3), 1221–1228.WanatJ.1974Bases of Budgetary Incrementalism6831221122810.2307/1959158Search in Google Scholar
Wildavsky, A. (1964). The politics of the budgetary process. Boston: Little, Brown.WildavskyA.1964BostonLittle, BrownSearch in Google Scholar
Xu, X., Liu, H., & Yao, M. (2019). Recent Progress of Anomaly Detection. Complexity, 1–11. https://doi.org/10.1155/2019/2686378XuX.LiuH.YaoM.2019Recent Progress of Anomaly Detection111https://doi.org/10.1155/2019/268637810.1155/2019/2686378Search in Google Scholar
Yamanishi, K., Takeuchi, J., Williams, G., & Milne, P. (2004). On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms. Data Mining and Knowledge Discovery, 8(3), 275–300. https://doi.org//10.1023/B:DAMI.0000023676.72185.7cYamanishiK.TakeuchiJ.WilliamsG.MilneP.2004On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms83275300https://doi.org//10.1023/B:DAMI.0000023676.72185.7c10.1145/347090.347160Search in Google Scholar
Zimek, A., & Filzmoser, P. (2018). There and back again: Outlier detection between statistical reasoning and data mining algorithms. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(6). https://doi.org/10.1002/widm.1280ZimekA.FilzmoserP.2018There and back again: Outlier detection between statistical reasoning and data mining algorithms86https://doi.org/10.1002/widm.128010.1002/widm.1280Search in Google Scholar