Multinomna logistička regresija u kreditnom skoringu

Andabaka, Zana

master's thesis

Multinomna logistička regresija u kreditnom skoringu

Andabaka, Zana (2017)

Josip Juraj Strossmayer University of Osijek
Department of Mathematics
Chair of Pure Mathematics
Probability and Mathematical Statistics Research Group

Cite this document

APA 6th Edition

Andabaka, Z. (2017). Multinomna logistička regresija u kreditnom skoringu (Master's thesis). Retrieved from https://urn.nsk.hr/urn:nbn:hr:126:427213

MLA 8th Edition

Andabaka, Zana. "Multinomna logistička regresija u kreditnom skoringu." Master's thesis, Josip Juraj Strossmayer University of Osijek, Department of Mathematics, 2017. https://urn.nsk.hr/urn:nbn:hr:126:427213

Chicago 17th Edition

Harvard

Andabaka, Z. (2017). 'Multinomna logistička regresija u kreditnom skoringu', Master's thesis, Josip Juraj Strossmayer University of Osijek, Department of Mathematics, accessed 04 August 2020, https://urn.nsk.hr/urn:nbn:hr:126:427213

Vancouver

Andabaka Z. Multinomna logistička regresija u kreditnom skoringu [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Department of Mathematics; 2017 [cited 2020 August 04] Available at: https://urn.nsk.hr/urn:nbn:hr:126:427213

IEEE

Z. Andabaka, "Multinomna logistička regresija u kreditnom skoringu", Master's thesis, Josip Juraj Strossmayer University of Osijek, Department of Mathematics, Osijek, 2017. Available at: https://urn.nsk.hr/urn:nbn:hr:126:427213

Cite this item: https://urn.nsk.hr/urn:nbn:hr:126:427213

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Metadata

Title	Multinomna logistička regresija u kreditnom skoringu
Author	Zana Andabaka
Mentor	Nataša Šarlija (mentor)
Mentor	Mirta Benšić (komentor)
Granter	Josip Juraj Strossmayer University of Osijek Department of Mathematics (Chair of Pure Mathematics) (Probability and Mathematical Statistics Research Group) Osijek
Defense date and country	2017-05, Croatia
Scientific / art field, discipline and subdiscipline	NATURAL SCIENCES Mathematics Financial and Business Mathematics
Abstract	Jedna od metoda kvantitativne kreditne analize je logistička regresija kojom se klijent svrštava u jednu od dvije kategorije, u dobre ili loše. Podaci o srednjim klijentima, iako postoje u bazi klijenata svake banke, uglavnom su isključeni u procesu kreditnog skoringa. Mnoge studije do sada nisu pokazale značajnost upotrebe srednjih klijenata pri kreiranju kredit skoring modela, uglavnom zbog toga sto je slabija granica među definicijama dobrih, srednjih i loših, te su konačne procjene manje točne od procjena koje bi dao logistički model u kojem su srednji izostavljeni. U ovom radu ispitat ćemo značajnost upotrebe srednjih na bazi klijenata jedne banke, na način da ćemo kreirati multinomni logistički model u kojem ovisna varijabla ima tri kategorije, dobre, srednje i loše, te binomni model samo s dobrim i lošim klijentima. Te ćemo usporediti rezultate.
Abstract (english)	One of quantitative credit analysis methods is logistic regression where the client is classified into the one of two categories, good or bad. Although there are data of poor clients in the database of any bank. They are generally excluded in the process of credit scoring. Many studies so far haven’t shown the importance of using poor clients in credit scoring model, mainly because the lower boundary between definitions of good, poor and bad, so the final estimates are less accurate than estimates obtained by logistic model in which poor are omitted. In this article we will examine the significance of use poor clients of a bank, in a way that will create a multinomial logistic model in which the dependent variable has three categories, good, poor and bad, and the binomial model only with good and bad customers. Than the results will be compared.
Keywords
Keywords (english)
Language	croatian
URN:NBN	urn:nbn:hr:126:427213
Study programme	Title: Mathematics; specializations in: Financial and Statistical Mathematics, Mathematics and Computer Science, Industrial and Applied Mathematics Course: Financial and Statistical Mathematics Study programme type: university Study level: graduate Academic / professional title: magistar/magistra matematike (magistar/magistra matematike)
Type of resource	Text
File origin	Born digital
Access conditions	Open access
Terms of use
Created on	2017-05-04 06:34:11