已複製連結
Inference for log-gamma distribution based on progressively type-II censored data
| 資料類型 | 期刊論文 |
|---|---|
| 題名(外文) | Inference for log-gamma distribution based on progressively type-II censored data |
| 作者 | |
| 出版年月 | 2006/06 |
| 刊名(外文) | Communications in Statistics - Theory and Methods（另開新視窗） |
| 卷 | 35 |
| 期 | 7 |
| 頁次 | 1271-1292 |
| 出版者 | Taylor & Francis（另開新視窗） |
| 關鍵字(外文) | Approximate maximum likelihood estimators（另開新視窗）; EM algorithm（另開新視窗）; Extreme value distribution（另開新視窗）; Fisher information（另開新視窗）; Fixed-point iteration（另開新視窗）; Maximum likelihood estimators（另開新視窗）; Modified EM algorithm（另開新視窗）; Monte Carlo simulations（另開新視窗）; Newton–Raphson method（另開新視窗）; Normal distribution（另開新視窗）; Pivotal quantities（另開新視窗）; Probability coverages（另開新視窗） |
| 摘要(外文) | We discuss the maximum likelihood estimates (MLEs) of the parameters of the log-gamma distribution based on progressively Type-II censored samples. We use the profile likelihood approach to tackle the problem of the estimation of the shape parameter κ. We derive approximate maximum likelihood estimators of the parameters μ and σ and use them as initial values in the determination of the MLEs through the Newton–Raphson method. Next, we discuss the EM algorithm and propose a modified EM algorithm for the determination of the MLEs. A simulation study is conducted to evaluate the bias and mean square error of these estimators and examine their behavior as the progressive censoring scheme and the shape parameter vary. We also discuss the interval estimation of the parameters μ and σ and show that the intervals based on the asymptotic normality of MLEs have very poor probability coverages for small values of m. Finally, we present two examples to illustrate all the methods of inference discussed in this paper. |