High-Dimensional Statistics with a View Toward Applications in Biology

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 model...

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Bibliographic Details
Published in:Annual review of statistics and its application 2014-01, Vol.1 (1), p.255-278
Main Authors: Bühlmann, Peter, Kalisch, Markus, Meier, Lukas
Format: Article
Language:English
Subjects:
causal inference
graphical modeling
multiple testing
penalized estimation
regression
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Summary: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 using a high-throughput genomic data set about riboflavin production with Bacillus subtilis , which we make publicly available for the first time.
ISSN:2326-8298
2326-831X
DOI:10.1146/annurev-statistics-022513-115545