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Monte carlo methods for bayesian inference on the linear hazard rate distribution
| Category | 期刊論文 |
|---|---|
| 題名(外文) | Monte carlo methods for bayesian inference on the linear hazard rate distribution |
| 作者 | |
| 出版年月 | 2006/09 |
| 刊名(外文) | Communications in Statistics - Simulation and Computation（opens in a new window） |
| 卷 | 35 |
| 期 | 3 |
| 頁次 | 575-590 |
| 出版者 | Taylor & Francis（opens in a new window） |
| 關鍵字(外文) | Bayesian computation（opens in a new window）; General progressive Type-II censoring（opens in a new window）; Markov Chain Monte Carlo (MCMC) method（opens in a new window）; Prediction（opens in a new window）; Simulation（opens in a new window） |
| 摘要(外文) | The Bayesian estimation and prediction problems for the linear hazard rate distribution under general progressively Type-II censored samples are considered in this article. The conventional Bayesian framework as well as the Markov Chain Monte Carlo (MCMC) method to generate the Bayesian conditional probabilities of interest are discussed. Sensitivity of the prior for the model is also examined. The flood data on Fox River, Wisconsin, from 1918 to 1950, are used to illustrate all the methods of inference discussed in this article. |