指数化泰西尔分布中最大似然估计的偏差减小

Ahmed Abdulhadi Ahmed, Z. Algamal, Olayan Albalawi
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引用次数: 0

摘要

指数化泰西尔分布(ETD)为生存数据建模提供了另一种选择,它考虑到了为具有递增和递减危险率函数的数据建模的灵活性。最常用的 ETD 分布参数估计方法是最大似然估计法(MLE)。另一方面,由于样本量较小,MLE 的偏差是众所周知的。因此,我们需要为 ETD 参数生成几乎无偏的估计值。更具体地说,我们将重点放在两种偏差校正方法上,即引导法和分析法,以将 MLE 偏差减小到二阶偏差。我们通过蒙特卡罗模拟和两个实际数据应用对这些方法的性能进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bias reduction of maximum likelihood estimation in exponentiated Teissier distribution
The exponentiated Teissier distribution (ETD) offers an alternative for modeling survival data, taking into account flexibility in modeling data with increasing and decreasing hazard rate functions. The most popular method for parameter estimation of the ETD distribution is the maximum likelihood estimation (MLE). The MLE, on the other hand, is notoriously biased for its small sample sizes. We are therefore driven to generate virtually unbiased estimators for ETD parameters. More specifically, we focus on two methods of bias correction, bootstrapping and analytical approaches, to reduce MLE biases to the second order of bias. The performances of these approaches are compared through Monte Carlo simulations and two real-data applications.
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