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基于贝叶斯标准的变量选择.

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此摘要是机器生成的。

偏差信息标准 (DIC) 经常超越模型,在贝叶斯模型选择中显示高灵敏度但较差的正确选择率 (0-2%),即使采用较大的样本大小. 边际概率标准提供了更好的非对称性表现,避免了DIC持续存在的错误选择问题.

关键词:
偏差信息标准 偏差信息标准最高的后面模型g-prior 在此之前错误的选择错误的选择

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科学领域:

  • 统计 统计 统计 统计
  • 计算统计学 计算统计学
  • 贝叶斯的推理是贝叶斯的推理.

背景情况:

  • 贝叶斯模型选择通常采用诸如最高后方模型 (HPM) 和偏差信息标准 (DIC) 等标准.
  • DIC被广泛使用,因为它很容易通过采样方法进行估计,并且可以在贝叶斯软件中使用.
  • 在模型选择准确性方面,DIC的实际实用性与其理论性能形成对比.

研究的目的:

  • 评估基于贝叶斯标准的模型选择中的偏差信息标准 (DIC) 和边际概率的性能.
  • 在不同样本大小中调查DIC的灵敏度和正确的选择率.
  • 为了比较DIC和边际概率的非对称性质,特别是在错误选择概率方面.

主要方法:

  • 基于贝叶斯标准的选择方法的分析,重点是最高后置模型 (HPM) 和偏差信息标准 (DIC).
  • 对DIC和边际概率的非对称行为和错误选择概率的理论分析.
  • 在非小细胞肺癌患者数据中对生物标记物选择问题的模拟研究和应用.

主要成果:

  • DIC具有高灵敏度 (90-100%),但非常低的正确选择率 (0-2%),在所有样本大小中一致.
  • 无论是DIC还是边际概率,都会对不合适的模型进行异常惩罚.
  • 在具有特定先验的线性模型中,DIC的错误选择概率是远离零的,不同于边际概率,它可以趋于零.
  • DIC未能在数据生成,过度装配甚至两个过度装配模型之间进行非对称的区别.

结论:

  • 偏差信息标准 (DIC) 在准确的贝叶斯模型选择中表现出显著的局限性,通常偏爱过度配套的模型.
  • 与DIC相比,边际概率标准为模型选择提供了优越的非对称性质.
  • 研究结果强调在使用DIC时需要谨慎,特别是在复杂的模型中或当精确的模型选择至关重要时,需要考虑HPM或边际概率等替代方案.