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Machine Learning Survival Models restrictions: the case of startups time to failed with collinearity-related issues

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  • Diego Vallarino

    (Independent Researcher, Spain)

Abstract

This research evaluates the efficacy of survival models in forecasting startup failures and investigates their economic implications. Several machine learning survival models, including Kernel SVM, DeepSurv, Survival Random Forest, and MTLR, are assessed using the concordance index (C-index) as a measure of prediction accuracy. The findings reveal that more sophisticated models, such as Multi-Task Logical Regression (MTLR) and Random Forest, outperform the standard Cox and Kaplan Meier (K-M) models in terms of predicted accuracy.

Suggested Citation

  • Diego Vallarino, 2023. "Machine Learning Survival Models restrictions: the case of startups time to failed with collinearity-related issues," Journal of Economic Statistics, Anser Press, vol. 1(3), pages 1-15, December.
    • Handle: RePEc:bba:j00005:v:1:y:2023:i:3:p:1-15:d:264
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    References listed on IDEAS

    as
    1. Luoma, M & Laitinen, EK, 1991. "Survival analysis as a tool for company failure prediction," Omega, Elsevier, vol. 19(6), pages 673-678.
    2. Godlewski, Christophe J., 2015. "The dynamics of bank debt renegotiation in Europe: A survival analysis approach," Economic Modelling, Elsevier, vol. 49(C), pages 19-31.
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