Statistics for High-Dimensional Data: Methods, Theory and Applications, Peter Buhlman and Sara van de Geer著, Springer, 2011年6月, 558pp., 価格 114,99〓, ISBN 978-3642201912

Statistical Foundations of Data ScienceStatistical Inference from High Dimensional DataHigh-dimensional Data AnalysisIntroduction to High-Dimensional StatisticsStatistics for High-Dimensional DataHigh-Dimensional Covariance Matrix EstimationComputer Age Statistical Inference, Student EditionThe Grammar of GraphicsMathematical Foundations of Infinite-Dimensional Statistical ModelsFeature Selection for High-Dimensional DataPrinciples and Methods for Data ScienceAnalysis of Multivariate and High-Dimensional DataNonlinear Dimensionality ReductionIndependence Screening in High-Dimensional DataHigh-dimensional StatisticsData Analysis for the Life Sciences with RFunctional and HighDimensional Statistics and Related FieldsHigh-Dimensional ProbabilityStatistics for High-Dimensional DataHigh-dimensional Regression ModelingHigh-Dimensional StatisticsAnalyzing High-Dimensional Gene Expression and DNA Methylation Data with RStatistical Analysis for High-Dimensional DataMultivariate Statistical AnalysisFoundations of Data ScienceStatistical Diagnostics for CancerIntroduction to High-Dimensional StatisticsMultivariate StatisticsGeometric Structure of HighDimensional Data and Dimensionality ReductionProbability and Statistics for Computer ScienceLarge Sample Covariance Matrices and High-Dimensional Data AnalysisConcentration of Maxima and Fundamental Limits in High-Dimensional Testing and InferenceApplied Biclustering Methods for Big and High-Dimensional Data Using RHigh-Dimensional ProbabilityIntroduction to High-Dimensional StatisticsHigh-Dimensional Covariance EstimationExploration and Analysis of DNA Microarray and Other High-Dimensional DataDynamic Regression Models for Survival DataHigh Dimensional Econometrics and IdentificationHigh-Dimensional Covariance Estimation