以二项式时间序列数据为基础的基于铜的马尔科夫链物流回归建模
Pepi Novianti1,2, Gunardi1, Dedi Rosadi1
1Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia.
MethodsX
|January 3, 2024
概括
本研究介绍了一种基于的马尔科夫链逻辑回归模型,用于带有共变量的二项式时间序列数据. 最大概率估计 (MLE) 准确估计模型参数,揭示变量关系和时间依赖.
科学领域:
- 统计 统计 统计 统计
- 时间序列分析时间序列分析
- 统计建模 统计建模
背景情况:
- 传统的时间序列模型经常与二项式数据和共变量包含作斗争.
- 偶数函数提供了一种灵活的方式来建模联合分布和依赖关系.
- 马尔科夫链模型捕获数据中的顺序依赖关系.
研究的目的:
- 为二项式时间序列数据开发基于的马尔科夫链逻辑回归模型.
- 将共变量纳入时间序列模型.
- 使用最大概率估计 (MLE) 估计模型参数,包括后勤回归和形参数.
主要方法:
- 利用基于的马尔科夫链方法来建模二项式时间序列.
- 集成后勤回归用于用共变量建模成功概率.
- 在参数估计中使用双变量函数 (克莱顿,冈贝尔,弗兰克) 和最大概率估计 (MLE).
主要成果:
- MLE证明了对拟议模型的准确参数估计.
- 该模型有效地捕捉了依赖和独立变量之间的关系.
- 该模型成功估计了二项式时间序列数据的时间依赖性.
结论:
- 基于的马尔科夫链逻辑回归模型是分析具有共变量的二项式时间序列的可行方法.
- 在这种复杂的模型中,MLE是一种高效的参数估计方法.
- 该模型提供了对变量关联和时间动态的洞察.
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