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

Statistical Applications in Genetics and Molecular Biology 2008, 7:article 35. The objective of dimension reduction is to make analysis and interpretation easier, while at the same time retaining most of the information (variation) contained in the data. A summary of the performance of the methods applied to both published microarray data sets and to simulated data is given. Probability theory and its applications. We introduce a method to associate multiple repeatedly measured intermediate risk factors with a high dimensional set of single nucleotide polymorphisms (SNPs). Both these applications involve electromagnetic spectra, but the scope of spectral data modeling is much broader: auditory spectra and chemical chromatography data also fit the same framework as the general problem considered here. Van de Geer http://www.eecs.berkeley.edu/~wainwrig/Papers/WaiJor08_FTML.pdf. One of the most challenging problems in modern statistics is to find effective methods for the analysis of complex multi-dimensional data. Many fields of This feature sets such spectral data apart from the typical statistical high-dimensional data set and leads to considering methods that fit models in which the coefficients are smooth functions of the wavelengths to which they apply. Dr Julia Brettschneider, Statistical methodology for high-dimensional molecular data, methodology for statistical analysis of high-throughput genomic and proteomic data. Statistics for High-Dimensional Data: Methods, Theory and Applications By P. Clearly, the Finally, we introduce and include in the summary a novel application of statistical entropy to provide a new heuristic measure of the number of interpretable components. If gesture facilitates learning by eliciting certain eye movements, then this would advance theory and provide educators with a guiding principle for designing learning materials. BigData: Probabilistic Methods for Efficient Search and Statistical Learning in Extremely High-Dimensional Data – November 30. 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.