一个非线性整数值自回归模型,使用零膨胀数据序列
Predrag M Popović1, Hassan S Bakouch2, Miroslav M Ristić3
1Faculty of Civil Engineering and Architecture, University of Niš, Niš, Serbia.
Journal of applied statistics
|April 30, 2025
概括
一个新的非线性静止过程模型使用生存和创新组件计算时间序列. 这种灵活的模型解决了多余的零点,并与真实世界的数据进行了验证,证明了它的适应性.
科学领域:
- 统计 统计 统计 统计
- 时间序列分析时间序列分析
- 随机过程 随机过程
背景情况:
- 计数时间序列数据经常显示过多的零,这给标准模型带来了挑战.
- 现有的模型可能无法充分捕捉零膨胀或零放缓计数数据的复杂动态.
- 需要灵活的模型,可以在计数时间序列中容纳各种零模式.
研究的目的:
- 引入一个新的非线性静止过程,用于计数时间序列.
- 开发一种能够处理多余零点 (通货膨胀和通缩) 的模型.
- 调查拟议过程的参数估计方法.
主要方法:
- 拟议的过程整合了生存和创新组件.
- 存活组件使用了通用零修改的几何稀释操作员.
- 研究了各种概率分布的创新过程.
- 条件最大概率和条件最小平方用于参数估计.
主要成果:
- 新的过程有效地模拟了计数的时间序列,包括那些过多的零.
- 该模型在调整观察到的零通胀和通缩方面表现出灵活性.
- 研究了参数估计方法,并证明适用.
结论:
- 引入的非线性静止过程为计数时间序列分析提供了强大的框架.
- 该模型适应不同零模式的适应性使其适用于各种现实应用.
- 该研究提供了对计数数据的模型拟合和参数选择的实用见解.
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