Bayesian Inference for a Hidden Truncated Bivariate Exponential Distribution with Applicationsopen access
- Authors
- Ghosh, Indranil; Ng, Hon Keung Tony; Kim, Kipum; Kim, Seong W.
- Issue Date
- Mar-2024
- Publisher
- MDPI AG
- Keywords
- Bayes factor; bivariate exponential distribution; Gibbs sampling; hidden truncation; informative prior; posterior probability
- Citation
- Axioms, v.13, no.3, pp 1 - 18
- Pages
- 18
- Indexed
- SCIE
- Journal Title
- Axioms
- Volume
- 13
- Number
- 3
- Start Page
- 1
- End Page
- 18
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/118704
- DOI
- 10.3390/axioms13030140
- ISSN
- 2075-1680
2075-1680
- Abstract
- In many real-life scenarios, one variable is observed only if the other concomitant variable or the set of concomitant variables (in the multivariate scenario) is truncated from below, above, or from a two-sided approach. Hidden truncation models have been applied to analyze data when bivariate or multivariate observations are subject to some form of truncation. While the statistical inference for hidden truncation models (truncation from above) under the frequentist and the Bayesian paradigms has been adequately discussed in the literature, the estimation of a two-sided hidden truncation model under the Bayesian framework has not yet been discussed. In this paper, we consider the Bayesian inference for a general two-sided hidden truncation model based on the Arnold-Strauss bivariate exponential distribution. In addition, a Bayesian model selection approach based on the Bayes factor to select between models without truncation, with truncation from below, from above, and two-sided truncation is also explored. An extensive simulation study is carried out for varying parameter choices under the conjugate prior set-up. For illustrative purposes, a real-life dataset is re-analyzed to demonstrate the applicability of the proposed methodology.
- Files in This Item
-
Go to Link
- Appears in
Collections - COLLEGE OF SCIENCE AND CONVERGENCE TECHNOLOGY > ERICA 수리데이터사이언스학과 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/118704)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.