具有吸收状态和错误指定的随机效应的有限马尔科夫链:对认知数据的应用
Pei Wang1, Changrui Liu2, Jiyeon Park3
1Department of Applied Statistics and Operations Research, Bowling Green State University, Bowling Green, Ohio, USA.
Biostatistics & epidemiology
|September 19, 2025
概括
这项研究引入了一种使用有限马尔科夫链分析纵向数据的新方法. 该方法改善了随机效应的估计,有助于识别具有吸收状态高风险的个体.
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
- 随机过程 随机过程
背景情况:
- 具有吸收状态的有限马尔科夫链对于分析纵向分类数据至关重要.
- 估计具有固定和随机效应的过渡概率是复杂的,因为有许多参数.
研究的目的:
- 开发一种可靠的方法来估计具有吸收状态的有限马尔科夫链中的固定和随机效应.
- 提高随机效应估计的准确性,以识别高风险个体和疾病进展.
主要方法:
- 在各种随机效应分布中使用边际模型来估计固定效应.
- 使用h-likelihood方法来估计基于固定效应估计的随机效应.
- 应用了来自Nun研究的纵向认知数据的方法.
主要成果:
- 固定效应估计显示在各种假设中具有稳定性.
- 随机效应分析显示对错误指定的随机效应分布 (例如,AIC,Q-Q图) 的敏感性.
- 拟议的方法允许验证随机效应假设和更准确的估计.
结论:
- 开发的方法增强了用吸收状态对纵向数据的分析.
- 精确估计的随机效应有助于识别具有吸收状态高风险的个体,并确定疾病进展率.
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