Exploring the Impact of Latent and Obscure Factors on Left-Censored Data: Bayesian Approaches and Case Study
Pragya Gupta,
Arvind Pandey,
David D. Hanagal and
Shikhar Tyagi ()
Additional contact information
Pragya Gupta: Central University of Rajasthan
Arvind Pandey: Central University of Rajasthan
David D. Hanagal: Savitri Bai Phule Pune University
Shikhar Tyagi: Central University of Rajasthan
A chapter in Reliability Engineering for Industrial Processes, 2024, pp 293-320 from Springer
Abstract:
Abstract In the realm of scientific investigation, traditional survival studies have historically focused on mitigating failures over time. However, when both observed and unobserved variables remain enigmatic, adverse consequences can arise. Frailty models offer a promising approach to understanding the effects of these latent factors. In this scholarly work, we hypothesize that frailty has a lasting impact on the reversed hazard rate. Notably, our research highlights the reliability of generalized Lindley frailty models, rooted in the generalized log-logistic type II distribution, as a robust framework for capturing the widespread influence of inherent variability. To estimate the associated parameters, we employ diverse loss functions such as SELF, MQSELF, and PLF within a Bayesian framework, forming the foundation for Markov Chain Monte Carlo methodology. We subsequently utilize Bayesian assessment strategies to assess the effectiveness of our proposed models. To illustrate their superiority, we employ data from renowned Australian twins as a demonstrative case study, establishing the innovative models’ advantages over those relying on inverse Gaussian and gamma frailty distributions. This study delves into the impact of hidden and obscure factors on left-censored data, utilizing Bayesian methodologies, with a specific emphasis on the application of generalized Lindley frailty models. Our findings contribute to a deeper understanding of survival analysis, particularly when dealing with complex and unobservable covariates.
Keywords: Bayesian estimation; Frailty model; Left censoring; Reversed hazard rate; Survival distributions (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
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:ssrchp:978-3-031-55048-5_19
Ordering information: This item can be ordered from
http://www.springer.com/9783031550485
DOI: 10.1007/978-3-031-55048-5_19
Access Statistics for this chapter
More chapters in Springer Series in Reliability Engineering from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().