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关于查特吉和其他人的评论. (2018): 在零膨胀负二项模型中对集团稀疏度进行修正的框架
Adam Iqbal1, Himel Mallick2, Emmanuel O Ogundimu1
1Department of Mathematical Sciences, Durham University, Durham, UK.
由于调整参数选择不当,对群规范化的零膨胀负二项式 (ZINB) 模型的GOOOGLE方法失败了. 使用真ZINB日志概率或grBAR估计器可以解决这个问题,从而实现可靠的组选择.
科学领域:
- 统计 统计 统计 统计
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 集团规范的零膨胀负二项式 (ZINB) 模型用于多余的零的高维数据.
- 查特吉等人使用的GOOOGLE方法. 曾为ZINB模型提出,但存在实施问题.
- 准确的群体稀疏性和特征选择在ZINB建模中至关重要.
研究的目的:
- 重新检查GOOOGLE方法并确定其调整参数选择的局限性.
- 为ZINB模型中调参数选择提出一个更正的方法.
- 为ZINB组选择引入更强大的替代估计器和R包.
主要方法:
- 对GOOOGLE实现的分析,重点是对高斯代用品的贝叶斯信息标准 (BIC).
- 模拟研究以根据不同的调参数选择标准评估组特异性.
- 开发和实施使用真实ZINB日志概率进行纠正的调整参数选择.
- 关于完全代组破碎的自适应 (grBAR) 估计器的建议.
主要成果:
- 最初的 GOOOGLE 实现的 BIC 倾向于不受惩罚的解决方案,导致零组特异性.
- 通过真实ZINB日志概率选择调整参数可以纠正组稀疏性问题.
- 在ZINB模型中,grBAR估计器在组选择方面表现出更好的稳定性.
- 一个开源的R包 (GRAZIMs) 发布,提供这些更正和替代方法.
结论:
- 对于ZINB模型来说,GOOOGLE方法对BIC计算的高斯替代品的依赖是有缺陷的.
- 在ZINB模型中,准确的组选择需要使用真实ZINB日志概率或强大的替代方案,如grBAR.
- 该GRAZIMs套件有助于可靠地应用ZINB数据分析的组规范化.
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