Lifetime analysis with monotonic degradation: a boosted first hitting time model based on a homogeneous gamma process

Clara Bertinelli Salucci (), Azzeddine Bakdi (), Ingrid Kristine Glad (), Bo Henry Lindqvist (), Erik Vanem () and Riccardo De Bin ()
Additional contact information
Clara Bertinelli Salucci: University of Oslo
Azzeddine Bakdi: Corvus Energy
Ingrid Kristine Glad: University of Oslo
Bo Henry Lindqvist: Norwegian University of Science and Technology
Erik Vanem: University of Oslo
Riccardo De Bin: University of Oslo

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2025, vol. 31, issue 2, No 3, 300-339

Abstract: Abstract In the context of time-to-event analysis, First hitting time methods consider the event occurrence as the ending point of some evolving process. The characteristics of the process are of great relevance for the analysis, which makes this class of models interesting and particularly suitable for applications where something about the degradation path is known. In cases where the degradation can only worsen, a monotonic process is the most suitable choice. This paper proposes a boosting algorithm for first hitting time models based on an underlying homogeneous gamma process to account for the monotonicity of the degradation trend. The predictive power and versatility of the algorithm are shown with real data examples from both engineering and biomedical applications, as well as with simulated examples.

Keywords: First hitting time; Gradient boosting; Homogeneous gamma process; Cox model; Time-to-event outcome (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10985-025-09648-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:lifeda:v:31:y:2025:i:2:d:10.1007_s10985-025-09648-z

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10985

DOI: 10.1007/s10985-025-09648-z

Access Statistics for this article

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data is currently edited by Mei-Ling Ting Lee

More articles in Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().