prikaz prve stranice dokumenta Multinomna logistička regresija u kreditnom skoringu
  Download
PDF 243.38 KB
master's thesis
Multinomna logistička regresija u kreditnom skoringu
Osijek: Josip Juraj Strossmayer University of Osijek, Department of Mathematics, 2017. urn:nbn:hr:126:427213
Andabaka, Zana
Josip Juraj Strossmayer University of Osijek
Department of Mathematics
Chair of Pure Mathematics and Mathematics Teaching
Probability, Mathematical Statistics and Data Science Research Group

Institutional repository: Repository of School of Applied Mathematics and Informatics

Cite this document

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

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

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

Andabaka, Z. (2017). 'Multinomna logistička regresija u kreditnom skoringu', Master's thesis, Josip Juraj Strossmayer University of Osijek, Department of Mathematics, accessed 04 January 2025, https://urn.nsk.hr/urn:nbn:hr:126:427213

Andabaka Z. Multinomna logistička regresija u kreditnom skoringu [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Department of Mathematics; 2017 [cited 2025 January 04] Available at: https://urn.nsk.hr/urn:nbn:hr:126:427213

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

Metadata
TitleMultinomna logistička regresija u kreditnom skoringu
AuthorZana Andabaka
MentorNataša Šarlija (mentor)
MentorMirta Benšić (komentor)
GranterJosip Juraj Strossmayer University of Osijek
Department of Mathematics
(Chair of Pure Mathematics and Mathematics Teaching)
(Probability, Mathematical Statistics and Data Science Research Group)
Osijek
Defense date and country2017-05, 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)
Languagecroatian
URN:NBNurn:nbn:hr:126:427213
Study programmeTitle: 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 resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2017-05-04 06:34:11