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Inference for log-gamma distribution based on progressively type-II censored data
| Category | 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（opens in a new window） |
| 卷 | 35 |
| 期 | 7 |
| 頁次 | 1271-1292 |
| 出版者 | Taylor & Francis（opens in a new window） |
| 關鍵字(外文) | Approximate maximum likelihood estimators（opens in a new window）; EM algorithm（opens in a new window）; Extreme value distribution（opens in a new window）; Fisher information（opens in a new window）; Fixed-point iteration（opens in a new window）; Maximum likelihood estimators（opens in a new window）; Modified EM algorithm（opens in a new window）; Monte Carlo simulations（opens in a new window）; Newton–Raphson method（opens in a new window）; Normal distribution（opens in a new window）; Pivotal quantities（opens in a new window）; Probability coverages（opens in a new window） |
| 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. |