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master's thesis
Analiza mjere pogreške predviđanja rizika modelom binomne logističke regresije
Zagreb: University of Zagreb, Faculty of Science, 2019. urn:nbn:hr:217:099842
Ruščić, Toni
University of Zagreb
Faculty of Science
Department of Mathematics

Institutional repository: Repository of the Faculty of Science

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Ruščić, T. (2019). Analiza mjere pogreške predviđanja rizika modelom binomne logističke regresije (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:099842

Ruščić, Toni. "Analiza mjere pogreške predviđanja rizika modelom binomne logističke regresije." Master's thesis, University of Zagreb, Faculty of Science, 2019. https://urn.nsk.hr/urn:nbn:hr:217:099842

Ruščić, Toni. "Analiza mjere pogreške predviđanja rizika modelom binomne logističke regresije." Master's thesis, University of Zagreb, Faculty of Science, 2019. https://urn.nsk.hr/urn:nbn:hr:217:099842

Ruščić, T. (2019). 'Analiza mjere pogreške predviđanja rizika modelom binomne logističke regresije', Master's thesis, University of Zagreb, Faculty of Science, accessed 01 June 2024, https://urn.nsk.hr/urn:nbn:hr:217:099842

Ruščić T. Analiza mjere pogreške predviđanja rizika modelom binomne logističke regresije [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2019 [cited 2024 June 01] Available at: https://urn.nsk.hr/urn:nbn:hr:217:099842

T. Ruščić, "Analiza mjere pogreške predviđanja rizika modelom binomne logističke regresije", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:217:099842

Metadata
TitleAnaliza mjere pogreške predviđanja rizika modelom binomne logističke regresije
AuthorToni Ruščić
MentorVesna Lužar-Stiffler (mentor)
Committee memberVesna Lužar-Stiffler (predsjednik povjerenstva)
Committee memberBojan Basrak (član povjerenstva)
Committee memberVanja Wagner (član povjerenstva)
Committee memberVjekoslav Kovač (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Science
(Department of Mathematics)
Zagreb
Defense date and country2019-07-16, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Mathematics
Abstract
U ovom diplomskom radu cilj je bio analizirati mjere pogreške predikcije rizika u bankama i kreditnim institucijama. Za dobivanje rezultata koristili smo metodu binomne logističke regresije. U prvom dijelu diplomskog rada opisana je povijest kreditnog bodovanja, statističke i nestatističke metode koje se mogu koristiti za predikciju rizika te općenito o generaliziranim linearnim modelima i logističkoj regresiji. U drugom dijelu diplomskog rada opisan je Monte Carlo eksperiment pomoću kojeg smo ispitali mjere pogreške predviđanja rizika pri raznim uvjetima kao što su distribucija prediktorskih varijabli, broj prediktorskih varijabli, veličina uzorka i korelacija između varijabli. Rezultati su pokazali da su najbolji modeli Gamma distribucije s većim parametrom oblika k te da korelacija jako utječe na statističke modele. Modeli u kojem su varijable neovisne te modeli s više prediktorski varijabli su pokazali bolju prediktivnu sposobnost.
Abstract (english)
In this thesis, the aim was to analyze risk measures in banks and credit institutions. We used the binomial logistic regression method to obtain results. The first part of the thesis deals with the history of credit scoring, statistical and nonstatistical methods that can be used for risk prediction and about generalized linear models and logistic regression in general. In the second part of the thesis, we have described the Monte Carlo experiment by which we tested risk estimation measures at various perspectives such as probability distribution of predictor variables, number of predictor variables, sample size, and correlation between variables. The results showed that the best models are when predictor variables have Gamma distribution with the greater shape parameter k and that the correlation strongly influences the statistical models. Models in which variables are independent and models with more predictor variables have shown better predictive capability.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:217:099842
Study programmeTitle: Finance and Business 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 on2019-12-02 12:27:50