论文标题
使用应用程序对日志分布的强大明确估计
Robust explicit estimation of the log-logistic distribution with applications
论文作者
论文摘要
通常根据经典方法（例如最大似然估计）估算对数分布的参数，而这些方法通常会在数据包含异常值时会导致严重的偏见估计。在本文中，我们考虑了几个替代估计量，这些估计量不仅具有闭合形式的表达式，而且在一定程度的数据污染中也很健壮。我们根据分解点研究了每个估计器的鲁棒性属性。这些估计量的有限样本性能和有效性是通过蒙特卡洛模拟和真实数据应用来评估的。数值结果表明，所提出的估计器的性能具有优惠的方式，即它们与数据的最大似然估计量相媲美而没有污染，并且在存在数据污染的情况下提供了卓越的性能。
The parameters of the log-logistic distribution are generally estimated based on classical methods such as maximum likelihood estimation, whereas these methods usually result in severe biased estimates when the data contain outliers. In this paper, we consider several alternative estimators, which not only have closed-form expressions, but also are quite robust to a certain level of data contamination. We investigate the robustness property of each estimator in terms of the breakdown point. The finite sample performance and effectiveness of these estimators are evaluated through Monte Carlo simulations and a real-data application. Numerical results demonstrate that the proposed estimators perform favorably in a manner that they are comparable with the maximum likelihood estimator for the data without contamination and that they provide superior performance in the presence of data contamination.
