IDEAS home Printed from https://ideas.repec.org/h/spr/ssrchp/978-3-031-55048-5_19.html
   My bibliography  Save this book chapter

Exploring the Impact of Latent and Obscure Factors on Left-Censored Data: Bayesian Approaches and Case Study

In: Reliability Engineering for Industrial Processes

Author

Listed:
  • 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
    Christ Deemed to Be University)

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.

Suggested Citation

  • Pragya Gupta & Arvind Pandey & David D. Hanagal & Shikhar Tyagi, 2024. "Exploring the Impact of Latent and Obscure Factors on Left-Censored Data: Bayesian Approaches and Case Study," Springer Series in Reliability Engineering, in: P. K. Kapur & Hoang Pham & Gurinder Singh & Vivek Kumar (ed.), Reliability Engineering for Industrial Processes, pages 293-320, Springer.
  • Handle: RePEc:spr:ssrchp:978-3-031-55048-5_19
    DOI: 10.1007/978-3-031-55048-5_19
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:ssrchp:978-3-031-55048-5_19. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.