环境驱动的双变整数值自动回归模型
Huiqiao Wang1,2, Christian H Weiß1
1Department of Mathematics and Statistics, Helmut Schmidt University, Holstenhofweg 85, 22043 Hamburg, Germany.
Entropy (Basel, Switzerland)
|February 23, 2024
概括
一个新的环境驱动的双变整数值自回归 (CuBINAR) 模型处理非静止计数数据. 这个模型捕捉了复杂的依赖关系,并被验证用于现实世界的销售数量分析.
科学领域:
- 统计 统计 统计 统计
- 计量经济学 计量经济学
- 时间序列分析时间序列分析
背景情况:
- 计数时间序列经常表现出非静止性和复杂的依赖性.
- 现有的模型可能无法充分捕捉联合动态和外部影响因素.
研究的目的:
- 提出一种新的环境驱动的双变整数值自回归 (CuBINAR) 模型.
- 用一个联合的分类序列来解决双变数时间序列中的非静止性.
- 改进低数数据模型,分析交叉依赖关系.
主要方法:
- 开发了 CuBINAR 模型,其中包含了定义状态的联合分类序列.
- 关键的随机性质的导出.
- 使用Yule-Walker和条件最大概率方法进行参数估计.
- 通过模拟进行一致性分析和有限样本性能评估.
主要成果:
- 库比纳模型有效地模拟非静止的双变数计数时间序列.
- 估计方法显示一致性.
- 模拟研究证实了模型的有限样本性能.
- 该模型成功地应用于现实世界的销售计数数据.
结论:
- 拟议的CuBINAR模型提供了一个灵活的框架来分析非静止的双变数计数数据.
- 它为了解受常见情况影响的计数过程提供了有价值的工具.
- 该模型在销售预测和营销分析等领域显示出实际实用性.
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