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Author
Wycinka Ewa (University of Gdansk, Poland), Jurkiewicz Tomasz (University of Gdansk, Poland)
Title
Survival Regression Models for Single Events and Competing Risks Based on Pseudoobservations
Source
Statistics in Transition, 2019, vol. 20, nr 1, s. 171-188, rys., tab., bibliogr. s. 187-188
Keyword
Analiza przeżycia, Dane cenzorowane, Ryzyko kredytowe
Survival analysis, Censored data, Credit risk
Note
summ.
Abstract
Survival data is a special type of data that measures the time to an event of interest. The most important feature of survival data is the presence of censored observations. An observation is said to be right-censored if the time of the observation is, for some reason, shorter than the time to the event. If no censoring occurs in the data, standard statistical models can be used to analyse the data. Pseudo-observations can replace censored observations and thereby allow standard statistical models to be used. In this paper, a pseudo-observation approach was applied to single-event and competing-risks analysis, with special attention paid to the properties of the pseudo-observations. In the empirical part of the study, the use of regression models based on pseudo-observations in credit-risk assessment was investigated. Default, defined as a delay in payment, was considered to be the event of interest, while prepayment of credit was treated as a possible competing risk. Credits that neither default nor are prepaid during the follow-up were censored observations. Typical application characteristics of the credit and creditor were the covariates in the regression model. In a sample of retail credits provided by a Polish financial institution, regression models based on pseudo-observations were built for the single-event and competing-risks approaches. Estimates and discriminatory power of these models were compared to the Cox PH and Fine-Gray models. (original abstract)
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The Main Library of the Cracow University of Economics
The Library of Warsaw School of Economics
The Library of University of Economics in Katowice
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ISSN
1234-7655
Language
eng
URI / DOI
http://dx.doi.org/10.21307/stattrans-2019-010
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