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High-Dimensional Statistics with a View Toward Applications in Biology

机译:多维统计及其在生物学中的应用

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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
机译:我们回顾了用于高维数据分析的统计方法,并特别关注了在控制误报(I型错误)和p值方面评估不确定性的最新进展。主要关注于回归模型,但我们还讨论了基于观测数据的图形建模和因果推断。我们使用来自枯草芽孢杆菌的核黄素生产的高通量基因组数据集,从统计软件R中使用各种软件包说明了概念和方法,这是我们首次公开发布

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