报道不足的时间变化的MINAR(1) 过程用于建模多变量计数系列
Zeynab Aghabazaz1, Iraj Kazemi2
1Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, USA.
概括
一个新的时间变化的多变量整数值自回归模型解决了非静止计数数据的不足报告. 这种统计模型,tvMINAR(1),保留了交叉相关性,并应用于COVID-19病例数据.
科学领域:
- 统计学,时间序列分析,计量经济学,生物统计学
背景情况:
- 非静止计数时间序列通常表现出报告不足,使准确的建模复杂化.
- 现有的模型可能无法充分捕捉多变量计数数据中的交叉相关性,特别是报告不足.
研究的目的:
- 引入一级的新型时间变化的多变量整数值自回归模型 (tvMINAR) 对于非静止的,相关的计数数据,潜在的报表不足.
- 开发一种方法,以维护交叉相关性,并使用维特比算法促进模型拟合.
主要方法:
- 开发一个tvMINAR(1) 模型,使用非对角的自回归概率网络来保持多变量序列交叉相关性.
- 使用维特比算法来导出全部概率,适应未报告数量的随机稀释运算符.
- 进行模拟研究以验证拟议模型的性能.
主要成果:
- 拟议的tvMINAR模型有效地处理非静止和相关的计数数据,即使报告不足.
- 模拟研究表明模型能够准确地捕捉底层数据生成过程.
- 应用于COVID-19的日常病例数据展示了模型在现实世界的场景中的实际实用性.
结论:
- tvMINAR模型提供了一个强大的框架,用于分析复杂的计数时间序列数据,而报告不足.
- 维特比算法和随机稀释操作员集成提高了该模型的适用性和计算效率.
- 通过后期预测检查进行模型比较,证实了拟议方法的有效性.
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