混合Poisson INGARCH模型与应用程序的诊断分析
Wenjie Dang1, Fukang Zhu1, Nuo Xu1
1School of Mathematics, Jilin University, Changchun, People's Republic of China.
Journal of applied statistics
|October 6, 2025
概括
本研究介绍了混合波桑整数值通用自回归条件异类 (INGARCH) 模型的局部影响分析. 这些方法有效地识别了计数时间序列数据中的影响点.
科学领域:
- 统计建模 统计建模
- 时间序列分析时间序列分析.
- 计量经济学 计量经济学
背景情况:
- 当地影响分析对于统计诊断至关重要.
- 混合波桑分布为计数数据提供了灵活性.
- 整数价值的通用自回归条件异种类型 (INGARCH) 模型处理计数时间序列.
研究的目的:
- 将局部影响分析应用于混合的Poisson INGARCH模型.
- 在计数时间序列中识别有影响力的观测.
- 评估拟议的诊断方法的性能.
主要方法:
- 使用预期-最大化算法进行参数估计.
- 应用了局部影响分析与一般化的库克距离和Q距离.
- 研究了四种扰动方案:案例重量,数据,添加值和尺度.
主要成果:
- 证明了使用拟议框架识别有影响力的点.
- 在混合Poisson INGARCH模型中验证了局部影响方法的有效性.
- 通过模拟和现实世界数据分析展示了实用的实用性.
结论:
- 当地影响方法是混合Poisson INGARCH模型的一个有价值的工具.
- 建议的诊断技术对于计数时间序列是可行的和有效的.
- 这种方法提高了统计推断在计数数据分析中的可靠性.
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