Introduction：
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 highdimensional data analysis. Contents:
Preface part i high-dimensional classification chapter 1 high-dimensional classification jianqing fan， yingying fan and yichao wu 1 introduction
2 elements of classifications
3 impact of dimensionality on classification
4 distance-based classification rules
5 feature selection by independence rule
6 loss-based classification
7 feature selection in loss-based classification
8 multi-category classification references chapter 2 flexible large margin classifiers yufeng liu and yichao wu 1 background on classification
2 the support vector machine： the margin formulation and the sv interpretation
3 regularization framework
4 some extensions of the svm： bounded constraint machine and the balancing svm
5 multicategory classifiers
6 probability estimation
7 conclusions and discussions
references part ii large-scale multiple testing
chapter 3 a compound decision-theoretic approach to large-scale multiple testing
t tony cai and wenguang sun 1 introduction
2 fdr controlling procedures based on p-values
3 oracle and adaptive compound decision rules for fdr control
4 simultaneous testing of grouped hypotheses
5 large-scale multiple testing under dependence
6 open problems
references part iii model building with variable selection
chapter 4 model building with variable selection ming yuan 1 introduction
2 why variable selection
3 classical approaches
4 bayesian and stochastic search
5 regularization
6 towards more interpretable models
7 further readings
references chapter 5 bayesian variable selection in regression with networked predictors
feng tai， wei pan and xiaotong shen 1 introduction
2 statistical models
3 estimation
4 results
5 discussion
references part iv high-dimensional statistics in genomics
chapter 6 high-dimensional statistics in genomics hongzhe li 1 introduction
2 identification of active transcription factors using time-course gene expression data
3 methods for analysis of genomic data with a graphical str
4 statistical methods in eqtl studies
5 discussion and future direction
references chapter 7 an overview on joint modeling of censored survival time and longitudinal data
runze li and jian-jian ren 1 introduction
2 survival data with longitudinal covariates
3 joint modeling with right censored data
4 joint modeling with interval censored data
5 further studies
references part v analysis of survival and longitudinal data
chapter 8 survival analysis with high-dimensional covariates bin nan 1 introduction
2 regularized cox regression
3 hierarchically penalized cox regression with grouped variables
4 regularized methods for the accelerated failure time model
5 tuning parameter selection and a concluding remark
references part vi sufficient dimension reduction in regression
chapter 9 sufficient dimension reduction in regression xiangrong yin 1 introduction
2 sufficient dimension reduction in regression
3 sufficient variable selection （svs)
4 sdr for correlated data and large-p-small-n
5 further discussion
references chapter 10 combining statistical procedures lihua chen and yuhong yang 1 introduction
2 combining for adaptation
3 combining procedures for improvement
4 concluding remarks
references subject index
author index
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