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基本説明
Intends to examine important issues arising from high-dimensional data analysis to explore key ideas for statistical inference and prediction.
Full Description
Over the last few years, significant developments have been taking place in high-dimensional data analysis, driven primarily by a wide range of applications in many fields such as genomics and signal processing. In particular, substantial advances have been made in the areas of feature selection, covariance estimation, classification and regression. This book intends to examine important issues arising from high-dimensional data analysis to explore key ideas for statistical inference and prediction.It is structured around topics on multiple hypothesis testing, feature selection, regression, classification, dimension reduction, as well as applications in survival analysis and biomedical research.The book will appeal to graduate students and new researchers interested in the plethora of opportunities available in high-dimensional data analysis.
Contents
High-Dimensional Classification: High-Dimensional Classification (J-Q Fan et al.); Flexible Large Margin Classifiers (Y-F Liu & Y-C Wu); Large-Scale Multiple Testing: Large-Scale Multiple Testing (T T Cai & W-G Sun); Model Building with Variable Selection: Model Building with Variable Selection (M Yuan); Bayesian Variable Selection in Regression with Networked Predictors (F Tai et al.); High-Dimensional Statistics in Genomics: High-Dimensional Statistics in Genomics (H-Z Li); An Overview on Joint Modeling of Censored Survival Time and Longitudinal Data (R-Z Li & J-J Ren); Analysis of Survival and Longitudinal Data: Survival Analysis with High-Dimensional Covariates (B Nan); Sufficient Dimension Reduction in Regression: Sufficient Dimension Reduction in Regression (X-R Yin); Combining Statistical Procedures (L-H Chen & Y-H Yang).
