从时间审查的数据中估计自我激发点过程
Philipp J Schneider1, Thomas A Weber1
1École Polytechnique Fédérale de Lausanne, Station 5, CH-1015 Lausanne, Switzerland.
Physical review. E
|August 16, 2023
概括
我们开发了一种新算法,即循环识别与样本校正 (RISC),以使用有限的数据准确估计自激点过程的参数. 这种方法改进了模拟抵达现象的现有技术.
科学领域:
- 统计 统计 统计 统计
- 随机过程 随机过程
- 数据分析 数据分析
背景情况:
- 自激点过程对于模拟到达现象至关重要,但难以确定.
- 参数估计因时间审查数据和垃圾箱计数而进一步复杂化.
研究的目的:
- 提出一种新的算法,用于从时间审查数据中估计自我激发点过程参数.
- 为了应对垃圾箱计数和有限的观察间隔所带来的挑战.
主要方法:
- 引入了循环识别与样本校正 (RISC) 算法.
- 采用代样本路径生成和对观察到的垃圾箱计数进行校正.
- 在每个代中更新过程参数以近似随机特征.
主要成果:
- 与现有方法相比,RISC算法显示出较高的有限样本近似误差.
- 数字实验证实了RISC框架的有效性.
- 通过条件强度重建内史是提高估计准确度的关键.
结论:
- RISC算法提供了一个强大的解决方案,用于参数估计,在自我激发的点过程与时间审查的数据.
- 准确的内史重建对于精确的参数估计至关重要.
- 这些发现提升了分析复杂到达现象的方法.
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