Variable selection in proportional hazards cure model with time-varying covariates, application to US bank failures^*

* An oral presentation of this paper was given at the 10th International Conference of the ERCIM WG on Computational and Methodological Statistics, Senate House, University of London, UK, 16–18 December 2017

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ABSTRACT

From a survival analysis perspective, bank failure data are often characterized by small default rates and heavy censoring. This empirical evidence can be explained by the existence of a subpopulation of banks likely immune from bankruptcy. In this regard, we use a mixture cure model to separate the factors with an influence on the susceptibility to default from the ones affecting the survival time of susceptible banks. In this paper, we extend a semi-parametric proportional hazards cure model to time-varying covariates and we propose a variable selection technique based on its penalized likelihood. By means of a simulation study, we show how this technique performs reasonably well. Finally, we illustrate an application to commercial bank failures in the United States over the period 2006–2016.