Statistics for High-Dimensional Data: Methods, Theory and Applications

Statistical Foundations of Data ScienceNew Perspectives and Challenges in Econophysics and SociophysicsStatistical Learning with SparsityMathematical Foundations of Infinite-Dimensional Statistical ModelsStatistics and Analysis of ShapesMultivariate StatisticsHigh-Dimensional Covariance EstimationPrinciples and Methods for Data ScienceHigh-Dimensional StatisticsHigh-Dimensional ProbabilityHandbook of Financial Econometrics and StatisticsStatistics for High-Dimensional DataRegularization in High-dimensional StatisticsAnalysis of Multivariate and High-Dimensional DataProbability and ComputingLarge Sample Covariance Matrices and High-Dimensional Data AnalysisHigh-Dimensional Data Analysis in Cancer ResearchHandbook of Big Data AnalyticsIntroduction to Clustering Large and High-Dimensional DataAnalyzing High-Dimensional Gene Expression and DNA Methylation Data with RSufficient Dimension ReductionHandbook of Big DataHandbook of Mixture AnalysisHigh Dimensional Probability VIStatistical Analysis for High-Dimensional DataHigh-Dimensional ProbabilitySpectral Analysis of Large Dimensional Random MatricesBig Data AnalyticsIntroduction to High-Dimensional StatisticsGeometric Structure of High-Dimensional Data and Dimensionality ReductionApplied Biclustering Methods for Big and High-Dimensional Data Using RBig and Complex Data AnalysisHigh-dimensional Data AnalysisFunctional Statistics and Related FieldsHandbook of Data VisualizationNonlinear Dimensionality ReductionModern Statistics for Modern BiologySparse Modeling for Image and Vision ProcessingModeling and Stochastic Learning for Forecasting in High DimensionsContributions to Fault Detection and Diagnosis with High-dimensional Data