Life cycle prediction and survival model construction of digital economy enterprises integrating survival analysis

Shulei Yin

International Journal of Data Science, 2025, vol. 10, issue 6, 1-19

Abstract: The life cycle of digital economy enterprises is affected by many complex and nonlinear factors. Traditional methods can only handle some simple linear relationships. This paper proposes an improved gradient boosting regression tree (GBRT) model to further enhance the prediction ability. Firstly, the Kaplan-Meier survival curve is used for descriptive statistics and exploratory analysis. Then, the accelerated failure time (AFT) model is used to model the enterprise life cycle. Finally, the GBRT model is used to predict the mean-square error (MSE) value, and it is compared with the MSE value of the linear regression model. The MSE value of the survival model is 0.015, much smaller than the MSE value of the linear regression model of 0.232, reflecting the superiority of the survival model.

Keywords: AFT; accelerated failure time; Kaplan-Meier survival curve; GBRT; gradient boosting regression tree; survival model; linear regression method. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=146190 (text/html)
Open Access

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:ids:ijdsci:v:10:y:2025:i:6:p:1-19

Access Statistics for this article

More articles in International Journal of Data Science from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().