High-Dimensional Statistics with a View Toward Applications in Biology
High-Dimensional Statistics with a View Toward Applications in Biology
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DOI：
10.1146/annurev-statistics-022513-115545
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发表时间：
2014-01-01
期刊：
影响因子：
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通讯作者：
Meier, Lukas
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文献类型：
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作者：
Buehlmann, Peter;Kalisch, Markus;Meier, Lukas
我们回顾了高维数据分析的统计方法，并特别关注在控制假阳性陈述（I 类错误）和 p 值方面评估不确定性的最新进展。主要重点是回归模型，但我们也讨论基于观测数据的图形建模和因果推断。我们使用统计软件 R 中的各种软件包，使用有关枯草芽孢杆菌生产核黄素的高通量基因组数据集来说明概念和方法，这是我们首次公开的。
We review statistical methods for high-dimensional data analysis and pay particular attention to recent developments for assessing uncertainties in terms of controlling false positive statements (type I error) and p-values. The main focus is on regression models, but we also discuss graphical modeling and causal inference based on observational data. We illustrate the concepts and methods with various packages from the statistical software R using a high-throughput genomic data set about riboflavin production with Bacillus subtilis, which we make publicly available for the first time.