在离散范式中估计真假假设和自适应错误发现率控制的比例
Aniket Biswas1, Gaurangadeb Chattopadhyay2
1Department of Statistics, Dibrugarh University, Dibrugarh, Assam, India.
Biometrical journal. Biometrische Zeitschrift
|February 15, 2024
概括
这项研究修改了对离散数据的Storey估计器,改进了对真零假设比例的估计. 新的适应性程序为基因表达研究中的错误发现率控制提供了显著的功率增长.
科学领域:
- 生物统计学 生物统计学
- 基因组学就是基因组学.
- 统计遗传学 统计遗传学
背景情况:
- 斯托雷对真假假设比例的估计器最初是为连续数据开发的.
- 准确估计真零假设对于统计推理至关重要,特别是在高维数据分析中.
- 当现有方法应用于离散数据框架时,可能缺乏最佳性能.
研究的目的:
- 修改Storey的估计器,以便在离散数据框架内应用.
- 开发一个改进的估计器,用于真正的零假设的比例.
- 加强统计程序来控制虚假发现率 (FDR).
主要方法:
- 对离散数据的斯托雷估计器进行修改.
- 使用拟议的估计器制定适应的本杰明-霍赫伯格和本杰明-霍赫伯格-海斯程序.
- 针对适应性本雅明-霍赫伯格程序的FDR控制的分析证明.
- 模拟实验用于评估性能和功率增长.
主要成果:
- 修改后的估计器在离散设置中更好地估计了真零假设的比例.
- 拟议的自适应程序证明了对错误发现率的分析控制.
- 模拟研究表明,新的自适应程序是保守的,并且比标准方法提供了相当大的功率增益.
- 该方法应用于现实世界HIV和甲基化基因表达数据集.
结论:
- 修改后的离散框架估计器提高了真零假设比例估计的准确性.
- 基于该估计器的自适应FDR控制程序在基因组研究中提供了更好的统计能力.
- 提出的方法为分析离散的高维数据,特别是基因表达分析提供了宝贵的进步.
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