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- Title
A latent variable approach for modeling recall-based time-to-event data with Weibull distribution.
- Authors
Panwar, M. S.; Barnwal, Vikas; Yadav, C. P.
- Abstract
The ability of individuals to recall events is influenced by the time interval between the monitoring time and the occurrence of the event. In this article, we introduce a non-recall probability function that incorporates this information into our modeling framework. We model the time-to-event using the Weibull distribution and adopt a latent variable approach to handle situations where recall is not possible. In the classical framework, we obtain point estimators using expectation-maximization algorithm and construct the observed Fisher information matrix using missing information principle. Within the Bayesian paradigm, we derive point estimators under suitable choice of priors and calculate highest posterior density intervals using Markov Chain Monte Carlo samples. To assess the performance of the proposed estimators, we conduct an extensive simulation study. Additionally, we utilize age at menarche and breastfeeding datasets as examples to illustrate the effectiveness of the proposed methodology.
- Subjects
WEIBULL distribution; MARKOV chain Monte Carlo; LATENT variables; FISHER information; EXPECTATION-maximization algorithms; LATENT class analysis (Statistics)
- Publication
Computational Statistics, 2024, Vol 39, Issue 4, p2343
- ISSN
0943-4062
- Publication type
Article
- DOI
10.1007/s00180-023-01444-3