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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 by Peter Bühlmann, Sara van de Geer

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



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 ebook
Format: pdf
ISBN: 3642201911, 9783642201929
Page: 575
Publisher: Springer


Statistical Applications in Genetics and Molecular Biology 2008, 7:article 35. Li's research involves several fields of statistics, including high-dimensional data analysis, variable selection, and intensive longitudinal data analysis. The aim of the workshop is to bring together theory and practice in modeling and exploring structure in high-dimensional data. Monomorphic SNPs and SNPs with a missing percentage of 5% or more were deleted from further analysis, remaining missing data were randomly imputed based only on the marginal distribution of the SNP in all other individuals. We introduce a method to associate multiple repeatedly measured intermediate risk factors with a high dimensional set of single nucleotide polymorphisms (SNPs). A necessary step when building a classifier for high-dimensional data is feature selection. BigData: Probabilistic Methods for Efficient Search and Statistical Learning in Extremely High-Dimensional Data – November 30. Nearest centroid classifiers have recently been successfully employed in high-dimensional applications. 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. Feature selection is typically carried out by computing univariate statistics for each feature individually, without consideration for how a subset of features performs as a whole.

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