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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

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Statistics for High-Dimensional Data: Methods, Theory and Applications Peter Bühlmann, Sara van de Geer ebook
ISBN: 3642201911, 9783642201929
Publisher: Springer
Format: pdf
Page: 575

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. 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. ^ In addition to the theoretical study of the plug-in rule, we propose and study a new methodology for classification and variable selection based on adaboost. Nearest centroid classifiers have recently been successfully employed in high-dimensional applications. 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. Methods, Theory and Applications. 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. Bühlmann, Peter, van de Geer, Sara. Download Free eBook:Advances in Data, Methods, Models and Their Applications in Geoscience - Free chm, pdf ebooks rapidshare download, ebook torrents bittorrent download. 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. 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. Free download ebook Statistics for High-Dimensional Data: Methods, Theory and Applications (Springer Series in Statistics) pdf. Statistics for High-Dimensional Data. Van de Geer 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. Statistics for High-Dimensional Data: Methods, Theory and Applications By P. 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. Series: Springer Series in Statistics. A necessary step when building a classifier for high-dimensional data is feature selection.

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