面板计数数据的条件建模与部分间隔审查的故障事件
Xiangbin Hu1, Wen Su2, Zhisheng Ye3
1Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong.
Biometrics
|March 18, 2024
概括
这项研究引入了一种新的统计模型,用于分析纵向研究中的反复事件,并考虑信息失败事件. 该方法提高了对影响事件复发和故障时间的因素的理解.
科学领域:
- 生物统计学 生物统计学
- 纵向数据分析 纵向数据分析
- 生存分析的分析.
背景情况:
- 纵向研究中的面板计数数据通常涉及反复发生的事件.
- 部分间隔审查的故障事件可以提供关于反复发生事件的信息数据.
- 使用隐性变量模型的现有方法提供了对故障事件效应的间接解释.
研究的目的:
- 为面板计数数据提出一个新的统计模型,提供信息,部分间隔审查的故障事件.
- 开发一个估计程序,提供直接解释故障事件影响的估计程序.
- 解决现有统计方法对反复事件数据分析的局限性.
主要方法:
- 开发了一个失效时间依赖的比例平均值模型,具有未指定的链接函数.
- 使用二阶段估计程序,使用最小平方值的有条件预期.
- 使用B-spline函数来近似未知的基线平均值和链接函数,将故障时间分布视为麻烦参数.
主要成果:
- 拟议的方法允许直接解释故障事件对反复事件的影响.
- 理论导出确定了估计器的收率和非对称正常性.
- 广泛的模拟研究证实了有限样本的性能与理论结果一致.
结论:
- 开发的统计模型和估计程序有效地处理面板计数数据,提供信息,部分间隔审查的故障事件.
- 该方法提供了一种更直接,更易于解释的方式来分析故障事件对循环过程的影响.
- 这种方法在一项纵向健康长寿研究中得到了成功说明,并得出了有洞察力的结论.
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