超越时间同质性对于连续时间多态马尔科夫模型
Emmett B Kendall1, Jonathan P Williams1,2, Gudmund H Hermansen2,3,4
1Department of Statistics, North Carolina State University.
如果假定时间均,连续时间马尔科夫模型可能会有偏差. 本研究提出了一种时间不均模型的方法,提高了医疗记录等复杂数据的参数估计准确度.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 计算生物学 计算生物学
背景情况:
- 多态马尔科夫模型是随机过程的标准.
- 连续时间马尔科夫过程模型不规则地观察到的数据,在纵向研究中很常见.
- 时间均模型有分析解决方案,但时间不均模型没有.
研究的目的:
- 为了说明参数估计中的偏差,当假定时间均性时.
- 在真正的时间不均马尔科夫模型中提出一种概率计算方法.
- 解决多状态马尔科夫模型中的状态标签错误分类问题.
主要方法:
- 从科尔莫戈罗夫前期方程中推导概率函数.
- 使用矩阵指数式解决方案用于时间均的过程.
- 倡导贝叶斯计算以避免MLEs的数值梯度近似.
主要成果:
- 证明潜在的参数估计偏差,当断片均假设被违反时.
- 开发一个时间不均概率计算的框架.
- 应用到具有状态错误分类的多状态马尔科夫模型.
结论:
- 断片时间同质性的假设可以导致显著的偏差.
- 对于精确建模复杂的随机过程,需要一种时间不均的方法.
- 贝叶斯方法为这些模型中的参数估计提供了一个有效的替代方案.
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