<電子ブック>
Statistical Analysis for High-Dimensional Data : The Abel Symposium 2014

責任表示
著者
本文言語
出版者
出版年
出版地
関連情報
概要 This book features research contributions from The Abel Symposium on Statistical Analysis for High Dimensional Data, held in Nyvêagar, Lofoten, Norway, in May 2014. The focus of the symposium was on s...tatistical and machine learning methodologies specifically developed for inference in "big data" situations, with particular reference to genomic applications. The contributors, who are among the most prominent researchers on the theory of statistics for high dimensional inference, present new theories and methods, as well as challenging applications and computational solutions. Specific themes include, among others, variable selection and screening, penalised regression, sparsity, thresholding, low dimensional structures, computational challenges, non-convex situations, learning graphical models, sparse covariance and precision matrices, semi- and non-parametric formulations, multiple testing, classification, factor models, clustering, and preselection. Highlighting cutting-edge research and casting light on future research directions, the contributions will benefit graduate students and researchers in computational biology, statistics and the machine learning community.続きを見る
目次 Some Themes in High-Dimensional Statistics: A. Frigessi et al
Laplace Appoximation in High-Dimensional Bayesian Regression: R. Barber, M. Drton et al
Preselection in Lasso-Type Analysis for Ultra-High Dimensional Genomic Exploration: L.C. Bergersen, I. Glad et al
Spectral Clustering and Block Models: a Review and a new Algorithm: S. Bhattacharyya et al
Bayesian Hierarchical Mixture Models: L. Bottelo et al
iBATCGH; Integrative Bayesian Analysis of Transcriptomic and CGH Data: Cassese, M. Vannucci et al
Models of Random Sparse Eigenmatrices and Bayesian Analysis of Multivariate Structure: A.J. Cron, M. West
Combining Single and Paired End RNA-seq Data for Differential Expression Analysis: F. Feng, T.Speed et al
An Imputation Method for Estimation the Learning Curve in Classification Problems: E. Laber et al
Baysian Feature Allocation Models for Tumor Heterogeneity: J. Lee, P. Mueller et al
Bayesian Penalty Mixing: The Case of a Non-Separable Penalty: V. Rockova et al
Confidence Intervals for Maximin Effects in Inhomogeneous Large Scale Data: D. Rothenhausler et al
Chisquare Confidence Sets in High-Dimensional Regression: S. van de Geer et al. .
続きを見る
冊子版へのリンク
本文を見る Full text available from Springer Mathematics and Statistics eBooks 2016 English/International

詳細

レコードID
主題
SSID
eISBN
登録日 2020.06.27
更新日 2020.06.28