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高维比例危险模型的测试和置信区间
Ethan X Fang1, Yang Ning1, Han Liu1
1Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08544, USA.
概括
本研究为高维比例危险模型引入了一种新的描述关系方法. 它允许对低维组件进行假设测试和置信区间,改善复杂的生存数据分析中的统计推理.
科学领域:
- 统计 统计 统计 统计
- 生存分析的分析.
- 高维数据分析 高维数据分析
背景情况:
- 高维比例危险模型对于分析复杂的生存数据至关重要.
- 在这些模型中推断的现有方法面临着低维元件的挑战.
- 脱关系原理为强大的统计推理提供了一个有希望的途径.
研究的目的:
- 为假设测试和置信区间构建提出一种基于对应关系的方法.
- 开发统计学上最优和异常正常的测试统计数据.
- 建立基线危险和生存功能的置信区间的程序.
主要方法:
- 几何预测原理用于开发脱相关得分,沃尔德和部分概率比率统计.
- 测试统计数据的非对称正常性证明,不假设模型选择的一致性.
- 开发新的分点可信度区间构建程序.
主要成果:
- 证明了拟议的脱相关试验统计数据的非对称正常性.
- 建立了新的统计测试的半参数优化.
- 成功开发和验证了关键生存功能的置信区间的程序.
结论:
- 基于折叠关系的方法为推断在高维比例危险模型中提供了一个强大的框架.
- 提出的方法实现半参数优化,并得到数值证据的支持.
- 这项工作推进了复杂生存数据分析的统计方法.
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