基于对功率对数分布的排列集采样的估计方法
Najwan Alsadat1, Amal S Hassan2, Mohammed Elgarhy3,4
1Department of Quantitative Analysis, College of Business Administration, King Saud University, P.O. Box 71115, 11587, Riyadh, Saudi Arabia.
Scientific reports
|July 31, 2024
概括
排列集合采样 (RSS) 提高了局限功率对数分布 (PLD) 的统计参数估计准确性. 使用RSS的最大产品间距估计比简单的随机抽样 (SRS) 方法更有效.
科学领域:
- 统计 统计 统计 统计
- 统计建模 统计建模
背景情况:
- 准确的统计参数估计对于数据分析至关重要.
- 排序集采样 (RSS) 提供了一种高效的数据收集方法,特别是在直接量化具有挑战的情况下.
- 有界功率对数分布 (PLD) 是最近的一种分布,适用于有界的真实世界数据.
研究的目的:
- 用排序集采样 (RSS) 估计有限功率对数分布 (PLD) 的三个参数.
- 为了比较RSS下的各种估计方法的效率与它们的简单随机抽样 (SRS) 对应的效率.
主要方法:
- 使用RSS估计PLD参数.
- 调查多种常规估计器:最大概率,最小间距变量,安德森-达林,最小平方,克拉默--米斯和最大间距产物.
- 通过模拟,对RSS估计与SRS估计进行比较分析.
- 验证使用现实世界数据的例子.
主要成果:
- 最大产品间距 (MPS) 估计方法显示了SRS和RSS数据集的卓越性能.
- 使用RSS获得的估计总是比通过SRS获得的更有效.
- 排序集采样 (RSS) 显著提高了PLD参数估计的准确性和效率.
结论:
- 排列集合采样 (RSS) 是一种有价值的技术,可以改进有限功率对数分布 (PLD) 的参数估计.
- 最大产品间距 (MPS) 方法,与RSS相结合,提供最有效的估计.
- 这些发现凸显了RSS估计器在现实应用中的实用实用性.
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