Open Access Journal Article

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

by Diego Vallarino a,*
a
Independent Researcher, Spain
*
Author to whom correspondence should be addressed.
JES  2023, 14; 1(3), 14; https://doi.org/10.58567/jes01030001
Received: 18 August 2023 / Accepted: 10 November 2023 / Published Online: 1 December 2023
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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.

Keywords: Startups Failure; Survival Analysis; Survival Machine Learning Models; Concordance Index;
Copyright: © 2023 by Vallarino. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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ACS Style
Vallarino, D. Machine Learning Survival Models restrictions: the case of startups time to failed with collinearity-related issues. Journal of Economic Statistics, 2023, 1, 14. https://doi.org/10.58567/jes01030001
AMA Style
Vallarino D. Machine Learning Survival Models restrictions: the case of startups time to failed with collinearity-related issues. Journal of Economic Statistics; 2023, 1(3):14. https://doi.org/10.58567/jes01030001
Chicago/Turabian Style
Vallarino, Diego 2023. "Machine Learning Survival Models restrictions: the case of startups time to failed with collinearity-related issues" Journal of Economic Statistics 1, no.3:14. https://doi.org/10.58567/jes01030001
APA style
Vallarino, D. (2023). Machine Learning Survival Models restrictions: the case of startups time to failed with collinearity-related issues. Journal of Economic Statistics, 1(3), 14. https://doi.org/10.58567/jes01030001

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