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Statistics for High-Dimensional Data: Methods, Theory and Applications Peter Bühlmann, Sara van de Geer
Publisher: Springer

The CMSS program will focus on statistical and computational methods applied to the social sciences, with a particular focus on up-to-date methods for exploiting the potential of large social science datasets. Statistics for High-Dimensional Data: Methods, Theory and Applications. Random matrices and free probability. Dr Julia Brettschneider, Statistical methodology for high-dimensional molecular data, methodology for statistical analysis of high-throughput genomic and proteomic data. Peter BГјhlmann, Sara van de Geer. Rings, algebras and modules (except . Probability theory and its applications. Digraphs : Theory, Algorithms, and Applications, 2nd ed / Jørgen Bang-Jensen and Gregory Z. Connections with sections 2, 3, 14, 15. Important fields with It will develop practical methods, efficient algorithms, statistical software, and solid theory for test of significance and confidence regions for low-dimensional functions of features, even when the dimension of data is high. Li's research involves several fields of statistics, including high-dimensional data analysis, variable selection, and intensive longitudinal data analysis. The three principal themes of the program The LDHD program will address methodological, theoretical, and computational challenges posed by high-dimensional mathematical and statistical models with limited data. Statistics for High-Dimensional Data : Methods, Theory, and Applications / Peter Bühlmann and Sara van de Geer. High-dimensional data is an area of intense current interest in statistical research and practice due to the rapid development of information technologies and their applications to modern scientific experiments. High-dimensional data analysis. Statistics.for.High.Dimensional.Data. Algebra (6-7 lectures) Groups and their representations (except as specified in 5 and 7).