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Statistics for High-Dimensional Data: Methods,
Statistics for High-Dimensional Data: Methods,

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

Statistics for High-Dimensional Data: Methods, Theory and Applications


Statistics.for.High.Dimensional.Data.Methods.Theory.and.Applications.pdf
ISBN: 3642201911,9783642201929 | 575 pages | 15 Mb


Download Statistics for High-Dimensional Data: Methods, Theory and Applications



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




Rings, algebras and modules (except . Random matrices and free probability. Probability theory and its applications. Bühlmann, Peter, van de Geer, Sara. High-dimensional data analysis. Our short course will introduce statistical issues and methods related to the analysis of genome-wide association data, copy number variation analysis, and analysis of rare variants and several important topics in human genetic research. Digraphs : Theory, Algorithms, and Applications, 2nd ed / Jørgen Bang-Jensen and Gregory Z. Free download ebook Statistics for High-Dimensional Data: Methods, Theory and Applications (Springer Series in Statistics) pdf. 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. Statistics for High-Dimensional Data. 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. Series: Springer Series in Statistics. Li's research involves several fields of statistics, including high-dimensional data analysis, variable selection, and intensive longitudinal data analysis. Methods, Theory and Applications. Connections with sections 2, 3, 14, 15. Statistics for High-Dimensional Data : Methods, Theory, and Applications / Peter Bühlmann and Sara van de Geer. Algebra (6-7 lectures) Groups and their representations (except as specified in 5 and 7). Dr Julia Brettschneider, Statistical methodology for high-dimensional molecular data, methodology for statistical analysis of high-throughput genomic and proteomic data. BigData: Probabilistic Methods for Efficient Search and Statistical Learning in Extremely High-Dimensional Data – November 30. Statistics for High-Dimensional Data: Methods, Theory and Applications By P. Van de Geer http://www.eecs.berkeley.edu/~wainwrig/Papers/WaiJor08_FTML.pdf.

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