用随机系数对零膨胀的几何INAR过程进行估计和预测方法的比较
R Nasirzadeh1, H Bakouch2,3
1Department of Statistics, Faculty of Science, Fasa University, Fasa, Iran.
Journal of applied statistics
|September 13, 2024
概括
本研究分析了零膨胀计数时间序列模型,重点关注过程. 模拟和现实世界的数据显示贝叶斯和Bootstrap预测方法提供优越的预测准确度,尽管计算时间较长.
科学领域:
- 统计 统计 统计 统计
- 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:
- 计数时间序列数据经常表现出过度分散,过多的零和自相关性,需要专门的模型.
- 零膨胀模型对于准确分析这些数据至关重要,特别是在生态学和金融等领域.
- 数学过程,一个具有随机系数和零膨胀几何分布的第一阶静态整数值自回归模型,提出了独特的分析挑战.
研究的目的:
- 为了调查和比较各种参数估计技术,以计算过程.
- 为零膨胀计数时间序列提出和评估新的预测方法.
- 通过模拟和现实世界的数据来评估不同的估计和预测策略的实际性能.
主要方法:
- 使用Whittle,Taper光谱Whittle,最大实证概率和Sieve引导估计器进行参数估计.
- 通过中位数,贝叶斯式和Sieve Bootstrap预测方法进行预测.
- 通过广泛的模拟研究和经验数据集分析进行性能评估.
主要成果:
- 所有调查的估计和预测方法都表现良好.
- 95%最高的预测概率间隔有效地涵盖了方法间观察到的数据.
- 贝叶斯和Sieve Bootstrap预测方法表现出卓越的预测准确性,证明了它们的计算成本.
结论:
- 数学过程适用于各种统计估计技术.
- 对于准确预测零膨胀计数时间序列,建议使用贝叶斯式和Sieve Bootstrap方法.
- 方法的选择涉及到计算效率和预测精度之间的权衡.
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