计数数据时间序列的整数值自回归模型的最新进展:全面审查
Vinitha Serrao1, Satyanarayana Poojari1, Asha Kamath1
1Department of Applied Statistics and Data Science, Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, Karnataka, India.
MethodsX
|February 9, 2026
概括
本综述探讨了计数数据时间序列的整数值自回归 (INAR) 模型的最新进展. 它强调了新的稀释运算符和灵活的分布,以更好地模拟复杂的数据特征.
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:
- 计数数据时间序列在金融,健康,经济学和环境科学中很普遍.
- 这些系列经常显示过度分散,低分散或零通货膨胀,使分析复杂化.
- 忽视这些特征会导致偏见的估计和有缺陷的统计推理.
研究的目的:
- 提供对近期整数值自回归模型 (INAR) 方法论发展的全面审查.
- 专注于在稀释操作员,估计技术和模型扩展方面的创新.
- 确定计数数据时间序列建模的研究缺口和未来方向.
主要方法:
- 斯科普斯和谷歌学者 (2010-2024) 的系统文献搜索.
- 审查INAR模型的方法进步.
- 对新型稀释运营商和创新分布的分析.
主要成果:
- 最近的研究强调了新的稀释运营商和灵活的创新分配.
- 开发统一的建模框架,同时处理多个计数数据特征.
- 确定目前INAR模型研究中的关键趋势和差距.
结论:
- 统一的INAR模型对于准确分析复杂计数数据至关重要.
- 需要进一步的研究来开发针对过度/低分散和零通货膨胀的可靠方法.
- 未来的工作应该专注于灵活的分布和先进的估计技术.
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