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2017, vol. 3, br. 2, str. 18-36
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Elementi multivarijantne analize kriminaliteta na oskudnim skupovima
Elements of multivariation analysis of crime on scarce meetings
Sažetak
Kriminalitet predstavlja štetnu pojavu koja je svojstvena svakoj kulturno-istorijskoj i društveno-ekonomskoj formaciji. Zbog toga ne treba da čudi postojano interesovanje koja vlada za kriminalitet u različitim oblastima društvenih nauka i života. Budući da kriminalitet predstavlja goruću temu mnogobrojnih naučnih i stručnih analiza ili istraživanja, može se zaključiti kako se radi o starom zajedničkom problemu svih država, nekadašnjih ili sadašnjih. Taj problem ne zavisi samo od njihovog društveno-političkog uređenja, već u istoj meri od stepena ekonomskog razvoja i karakterističnih društvenih činilaca i okolnosti koje postoje u odnosnim državama. Izvanredno velika raznovrsnost uzroka i okolnosti koji dovode do pojave kriminaliteta zahteva drugačiji pristup njegovom istraživanju. On je moguć i sastoji se u primeni multivarijantne analize, budući da ona omogućava višestrano sagledavanje kriminaliteta i izvođenje korisnih zaključaka koji mogu da posluže za njegovo bolje razumevanje i preduzimanje efikasnijih radnji koje imaju za cilj njegovo sprečavanje i omogućavanje normalnog funkcionisanja i razvoja države i društva. Primena multivarijante analize kriminaliteta u ovom radu zasniva se na razmatranjuposrednog (implicintnog) znanja kao metoda primenjenog na uzorku koji je napravljen na osnovu podataka iz četrdeset sedam država SAD. Zavisno od njihovih osobenosti, za svaku saveznu državu određene su različite stope kriminaliteta. Pomenuti uzorak korišćen je kako bi se primenom multivarijantne analize odgovorilo na osnovno pitanje: da li se njenom primenom mogu otkriti skriveni uzroci stope kriminaliteta koji nisu vidljivi iz podataka sadržanih u izabranom uzorku? Kako bi se postigao postavljeni cilj, takođe, korišćena je faktorska analiza kao metod,kao i metod inverzne faktorske(segmentacione) analize uzorka. Otkrivanje takvih skrivenih uzroka kriminaliteta moglo bi da se pokaže kao veoma važno za postizanje ukupne kontrole kriminaliteta. Budući da ukupna kontrola kriminaliteta predstavlja sasvim moderan pravac u savremenom pristupu borbi protiv kriminaliteta u svetu, očigledno je da njegova kontrola nijemoguća bez sagledavanja i takvih skrivenih činilaca (uzroka i uslova) koji pogoduju nastanku kriminalnih ponašanja, a koje može da pokaže upravo primena multivarijantna analiza zasnovana na pomenutom uzorku.
Abstract
Crime represents a harmful occurrence that is specific for each culture and socioeconomic formation. Due to this it is no surprise that there is an interest for crime in different areas of social sciences and life. Since crime is a burning topic of numerious scientific and expert analyses or researches, it can be concluded that it is an old mutual problem of all countries, old and new ones. It doesn't depend only on the sociopolitical order, but economic development and characteristical social factors and circumstances that exist in respective countries as well. Surprisingly large diversity of causes and circumstances that lead to the occurrence of crime demands a different approach to its research. It is possible and consists of the application of multivariate analysis, since it allows for many different ways of observing crime and taking more efficient actions aimed at its prevention and enabling of normal functioning and development of state and society. Application of multivariation analysis in crime, is based on consideration of indirect (implicit) knowledge as a method applied on a sample that consists of data from forty seven US countries. Depending on their individuality, different crime rates were determined for each federal state. The mentioned sample was used to answer a simple question through multivariation analysis: would its application discover hidden causes of crime rates, which aren't visible from the data contained in the selected sample? In order to achieve the set goal, factor analysis was used as well the method of inverse factoral (segmentation) sample analysis. Discovering such hidden samples of crime would be a very important way of achieving total control on it. Since full control of crime represents a very modern way in contemporary approach to fighting rime in the world, it is obvious that control is impossible without considering such hidden factors (causes and conditions) which favour occurrence of criminal behavior, which can be shown by multivariation analysis based on the aforementioned sample.
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Reference
|
|
|
*** (2000) Enron financial statements as of Decembar 31
|
|
1   
|
Abbott, L.J., Park, Y., Parker, S. (2000) The effects of audit committee activity and independence on corporate fraud. Managerial Finance, 26(11): 55-68
|
|
1
|
Bell, T.B., Carcello, J.V. (2000) A Decision Aid for Assessing the Likelihood of Fraudulent Financial Reporting. Auditing: A Journal of Practice & Theory, 19(1): 169-184
|
|
6
|
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J. (1984) Classification and regression trees. Belmont: Wadsworth
|
|
3
|
Breiman, L. (2001) Machine Learning, 45(1): 5-32
|
|
|
Chang, C.C., Lin, C.J. (2001) LIBSVM: A library for support vector machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm
|
|
1
|
Cherkassky, V., Mulier, F.M. (2007) Learning from data: Concepts, theory, and methods. John Wiley, 2nd edition
|
|
1
|
Cullinan, C.P., Sutton, S.G. (2002) Defrauding the public interest: A critical examination of reengineered audit processes and the likelihood of detecting fraud. Critical Perspectives on Accounting, 13(3): 297-310
|
|
|
Eining, M.M., Jones, D.R., Loebbecke, J.K. (1997) Reliance on decision aids: An examination of auditors' assessment of management fraud. Auditing: A Journal of Practice and Theory, 16(2), 1-19
|
|
|
Fanning, K.M., Cogger, K.O. (1998) Neural network detection of management fraud using published financial data. International Journal of Intelligent Systems in Accounting, Finance & Management, 7(1): 21-41
|
|
1
|
Green, B.P., Choi.J.H. (1997) Assessing the risk of management fraud through neural-network technology. Auditing: A Journal of Practice and Theory, 16(1); 8-28
|
|
1
|
Green, B.P., Choi.J.H. (1997) Assessing the risk of management fraud through neural-network technology. Auditing: A Journal of Practice and Theory, 16(1); 14-28
|
|
1
|
Kirkos, E., Spathis, C., Manolopoulos, Y. (2007) Data Mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32(4): 995-1003
|
|
4
|
Kohavi, R. (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. u: Proc. of International Joint Conference on Artificial Intelligence
|
|
2
|
Maindonald, J., Braun, J. (2007) Data analysis and graphics using R. Cambridge: Cambridge University Press, 2nd Edition
|
|
|
Persons, O.S. (2011) Using Financial Statement Data To Identify Factors Associated With Fraudulent Financial Reporting. Journal of Applied Business Research (JABR), 11(3): 38
|
|
1
|
Platt, J.C. (1998) Sequential minimal optimization: A fast algorithm for training support vector machines. Microsoft Research, Technical Report MSR-TR-98-8
|
|
|
Porter, B., Cameron, A. (1987) Company fraud-what price the auditor?. Accountant's Journal, 44-47
|
|
|
Pullam,, Reš.omon.Deborah (2002) How Three Unlikely Sleuths Exposed Fraud at World-Com. Wall Street Journal, 30); 1
|
|
2
|
Quinlan, J.R. (1996) Bagging, boosting and C4.5. u: AAAI-96 Fourteenth national Conference on Artificial Intelligence, Portland, OR, AAAI Press, Menlo Park, CA, Proc. of
|
|
|
SEC (2003) Accounting and auditing enforcement release. No. 1706, January 27
|
|
|
Spathis, C.T. (2002) Detecting false financial statements using published data: some evidence from Greece. Managerial Auditing Journal, 17(4): 179-191
|
|
1
|
Spathis, C., Doumpos, M., Zopounidis, C. (2003) Using client performance measures to identify pre-engagement factors associated with qualified audit reports in Greece. International Journal of Accounting, 38(3): 267-284
|
|
|
Stice, J. (1991) Using financial and market information to identify pre-engagement market factors associated with lawsuits against auditors. Accounting Review, 66(3); 516-533
|
|
|
Summers, S.L., Sweeney.J.T. (1998) Fraudulent misstated financial statements and insider trading: an empirical analysis. Accounting Review, 73(1); 131-146
|
|
6
|
Witten, I.H., Frank, E. (2005) Data mining: Practical machine learning tools and techniques with java implementations. San Francisco, CA, itd: Morgan Kaufmann
|
|
|
Wu, R.C. (1997) Neural Network Models: 'Fonudantion and application to an audit decision problem. Annals of Operations Research, 75: 291-301
|
|
|
|
|