在开发PIW4LIFETIME网络应用程序的过程中,对Poisson Inverse Weibull分布的尺度参数进行了签名日志概率测试
Sukanya Yodnual1, Jularat Chumnaul1
1Division of Computational Science, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla, Thailand.
PloS one
|August 1, 2025
概括
签名日志-概率比率测试 (SLRT) 提供了一种可靠的方法来分析使用波桑反向韦布尔 (PIW) 分布的系统故障时间. 它的表现优于最大概率估计器 (ANMLE) 的非对称正常性,特别是在小样本中,确保可靠的统计推理.
科学领域:
- 统计 统计 统计 统计
- 可靠性工程可靠性工程
- 生存分析的分析.
背景情况:
- 波桑反向韦布尔 (PIW) 分布为系统故障时间提供了灵活的模型.
- 对复杂分布中的参数进行假设测试对于准确的可靠性分析至关重要.
研究的目的:
- 介绍和评估PIW分布的尺度参数的签名日志-概率比测试 (SLRT).
- 将SLRT的性能与最大概率估计器 (ANMLE) 测试的非对称正常性进行比较.
主要方法:
- 进行模拟研究以评估I型错误率和实证功率.
- 在各种样本大小和参数配置中评估了SLRT的性能.
- 为了实际实施,开发了一个Web应用程序PIW4LIFETIME.
主要成果:
- SLRT 始终保持 Type I 错误率在可接受的范围内 (0.04-0.06) 在 0.05 显著程度.
- 与ANMLE相比,SLRT表现出更高的经验能力,特别是在小样本 (n=10,15) 中.
- ANMLE通常是保守的,在小样本中显示出较低的功率,尽管它的功率接近样本大小较大的SLRT.
结论:
- 在PIW分布中,SLRT是一种强大的,可靠的假设测试方法,其性能优于ANMLE,特别是在有限的数据的情况下.
- 开发的PIW4LIFETIME网络应用程序促进了SLRT用于分析系统故障数据的实际应用.
- 该研究强调了SLRT的有效性和PIW分布在可靠性工程中的实用性.
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