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

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. These are more than just interesting theoretical developments- they are now implemented in R packages such as SuperLearner and tmle. 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. In my PhD thesis ``Compressive System Identification (CSI): Theory and Applications of Exploiting Sparsity in the Analysis of High-Dimensional Dynamical Systems,'' I tried to combine the tools and ideas in Compressive Sensing and Sparse . 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. Nuit Blanche focuses on Compressive Sensing, Advanced Matrix Factorization Techniques as well as many other engaging ideas needed to handle and make sense of very high dimensional data also known as Big Data. Download Free eBook:Advances in Data, Methods, Models and Their Applications in Geoscience - Free chm, pdf ebooks rapidshare download, ebook torrents bittorrent download. High-dimensional data analysis. Rings, algebras and modules (except . Random matrices and free probability. The process of generating a hypothesis Alan's primarily works on the analysis of high dimensional data using semi-parametric statistical methods in applications such as using prognostic factors in severe trauma patients, the molecular biology of aging, and diarrheal disease in developing countries. 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. Statistics for High-Dimensional Data : Methods, Theory, and Applications / Peter Bühlmann and Sara van de Geer. Connections with sections 2, 3, 14, 15. Digraphs : Theory, Algorithms, and Applications, 2nd ed / Jørgen Bang-Jensen and Gregory Z. Algebra (6-7 lectures) Groups and their representations (except as specified in 5 and 7).