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

Diego Vallarino
Additional contact information
Diego Vallarino: Independent Researcher, Spain

Journal of Economic Statistics, 2023, vol. 1, issue 3, 1-15

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 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.anserpress.org/journal/jes/1/3/14/pdf (application/pdf)
https://www.anserpress.org/journal/jes/1/3/14 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bba:j00005:v:1:y:2023:i:3:p:1-15:d:264

Access Statistics for this article

Journal of Economic Statistics is currently edited by Ramona Wang

More articles in Journal of Economic Statistics from Anser Press
Bibliographic data for series maintained by Ramona Wang ().