Academic Journal
Bayesian and E-Bayesian Estimations of Bathtub-Shaped Distribution under Generalized Type-I Hybrid Censoring
| Title: | Bayesian and E-Bayesian Estimations of Bathtub-Shaped Distribution under Generalized Type-I Hybrid Censoring |
|---|---|
| Authors: | Yuxuan Zhang, Kaiwei Liu, Wenhao Gui |
| Source: | Entropy, Vol 23, Iss 8, p 934 (2021) |
| Publisher Information: | MDPI AG, 2021. |
| Publication Year: | 2021 |
| Collection: | LCC:Science LCC:Astrophysics LCC:Physics |
| Subject Terms: | bathtub-shaped distribution, Monte Carlo Markov Chain, E-Bayesian estimation, generalized Type-I hybrid censoring, Bayes estimation, Science, Astrophysics, QB460-466, Physics, QC1-999 |
| More Details: | For the purpose of improving the statistical efficiency of estimators in life-testing experiments, generalized Type-I hybrid censoring has lately been implemented by guaranteeing that experiments only terminate after a certain number of failures appear. With the wide applications of bathtub-shaped distribution in engineering areas and the recently introduced generalized Type-I hybrid censoring scheme, considering that there is no work coalescing this certain type of censoring model with a bathtub-shaped distribution, we consider the parameter inference under generalized Type-I hybrid censoring. First, estimations of the unknown scale parameter and the reliability function are obtained under the Bayesian method based on LINEX and squared error loss functions with a conjugate gamma prior. The comparison of estimations under the E-Bayesian method for different prior distributions and loss functions is analyzed. Additionally, Bayesian and E-Bayesian estimations with two unknown parameters are introduced. Furthermore, to verify the robustness of the estimations above, the Monte Carlo method is introduced for the simulation study. Finally, the application of the discussed inference in practice is illustrated by analyzing a real data set. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1099-4300 |
| Relation: | https://www.mdpi.com/1099-4300/23/8/934; https://doaj.org/toc/1099-4300 |
| DOI: | 10.3390/e23080934 |
| Access URL: | https://doaj.org/article/5c43255f3ab345128251db2182379b5b |
| Accession Number: | edsdoj.5c43255f3ab345128251db2182379b5b |
| Database: | Directory of Open Access Journals |
| Full text is not displayed to guests. | Login for full access. |
| ISSN: | 10994300 |
|---|---|
| DOI: | 10.3390/e23080934 |
| Published in: | Entropy |
| Language: | English |