Lore Dirick
Katholieke Universiteit Leuven
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Publication
Featured researches published by Lore Dirick.
European Journal of Operational Research | 2015
Lore Dirick; Gerda Claeskens; Bart Baesens
We derive the proper form of the Akaike information criterion for variable selection for mixture cure models, which are often fit via the expectation–maximization algorithm. Separate covariate sets may be used in the mixture components. The selection criteria are applicable to survival models for right-censored data with multiple competing risks and allow for the presence of a non-susceptible group. The method is illustrated on credit loan data, with pre-payment and default as events and maturity as the non-susceptible case and is used in a simulation study.
Journal of the Operational Research Society | 2017
Lore Dirick; Gerda Claeskens; Bart Baesens
We investigate the performance of various survival analysis techniques applied to ten actual credit data sets from Belgian and UK financial institutions. In the comparison we consider classical survival analysis techniques, namely the accelerated failure time models and Cox proportional hazards regression models, as well as Cox proportional hazards regression models with splines in the hazard function. Mixture cure models for single and multiple events were more recently introduced in the credit risk context. The performance of these models is evaluated using both a statistical evaluation and an economic approach through the use of annuity theory. It is found that spline-based methods and the single event mixture cure model perform well in the credit risk context.
Journal of Business & Economic Statistics | 2016
Lore Dirick; Gerda Claeskens; Bart Baesens
The prediction of the time of default in a credit risk setting via survival analysis needs to take a high censoring rate into account. This rate is because default does not occur for the majority of debtors. Mixture cure models allow the part of the loan population that is unsusceptible to default to be modeled, distinct from time of default for the susceptible population. In this article, we extend the mixture cure model to include time-varying covariates. We illustrate the method via simulations and by incorporating macro-economic factors as predictors for an actual bank dataset.
Proceedings of the Credit Scoring and Credit Control XIII conference | 2013
Lore Dirick; Gerda Claeskens; Bart Baesens
Proceedings of the Credit Scoring and Credit Control XIV conference | 2015
Lore Dirick; Gerda Claeskens; Bart Baesens
Archive | 2015
Lore Dirick; Gerda Claeskens; Bart Baesens
Archive | 2015
Lore Dirick; Gerda Claeskens; Bart Baesens
Archive | 2014
Lore Dirick; Gerda Claeskens; Andrey L. Vasnev; Bart Baesens
Archive | 2014
Lore Dirick; Gerda Claeskens; Andrey L. Vasnev; Bart Baesens
Archive | 2013
Lore Dirick; Gerda Claeskens; Bart Baesens