| Preface | |
| High-Dimensional Classification | |
| High-Dimensional Classification | p. 3 |
| Introduction | p. 3 |
| Elements of classifications | p. 4 |
| Impact of dimensionality on classification | p. 8 |
| Distance-based classification Rules | p. 14 |
| Feature selection by independence rule | p. 20 |
| Loss-based classification | p. 24 |
| Feature selection in loss-based classification | p. 27 |
| Multi-category classification | p. 31 |
| References | p. 34 |
| Flexible Large Margin Classifiers | p. 39 |
| Background on classification | p. 39 |
| The support vector machine: the margin formulation and the SV interpretation | p. 40 |
| Regularization framework | p. 45 |
| Some extensions of the SVM: Bounded constraint machine and the balancing SVM | p. 48 |
| Multicategory classifiers | p. 51 |
| Probability estimation | p. 62 |
| Conclusions and discussions | p. 66 |
| References | p. 67 |
| Large-Scale Multiple Testing | |
| A Compound Decision-Theoretic Approach to Large-Scale Multiple Testing | p. 75 |
| Introduction | p. 75 |
| FDR controlling procedures based on p-values | p. 79 |
| Oracle and adaptive compound decision rules for FDR control | p. 82 |
| Simultaneous testing of grouped hypotheses | p. 93 |
| Large-scale multiple testing under dependence | p. 102 |
| Open problems | p. 111 |
| References | p. 112 |
| Model Building with Variable Selection | |
| Model Building with Variable Selection | p. 119 |
| Introduction | p. 119 |
| Why variable selection | p. 120 |
| Classical approaches | p. 121 |
| Bayesian and stochastic search | p. 125 |
| Regularization | p. 128 |
| Towards more interpretable models | p. 134 |
| Further readings | p. 141 |
| References | p. 142 |
| Bayesian Variable Selection in Regression with Networked Predictors | p. 147 |
| Introduction | p. 147 |
| Statistical models | p. 149 |
| Estimation | p. 152 |
| Results | p. 154 |
| Discussion | p. 162 |
| References | p. 163 |
| High-Dimensional Statistics in Genomics | |
| High-Dimensional Statistics in Genomics | p. 169 |
| Introduction | p. 169 |
| Identification of active transcription factors using time-course gene expression data | p. 173 |
| Methods for analysis of genomic data with a graphical structure | p. 178 |
| Statistical methods in eQTL studies | p. 182 |
| Discussion and future direction | p. 187 |
| References | p. 188 |
| An Overview on Joint Modeling of Censored Survival Time and Longitudinal Data | p. 195 |
| Introduction | p. 195 |
| Survival data with longitudinal covariates | p. 197 |
| Joint modeling with right censored data | p. 202 |
| Joint modeling with interval censored data | p. 214 |
| Further studies | p. 215 |
| References | p. 215 |
| Analysis of Survival and Longitudinal Data | |
| Survival Analysis with High-Dimensional Covariates | p. 223 |
| Introduction | p. 223 |
| Regularized Cox regression | p. 227 |
| Hierarchically penalized Cox regression with grouped variables | p. 232 |
| Regularized methods for the accelerated failure time model | p. 247 |
| Tuning parameter selection and a concluding remark | p. 251 |
| References | p. 251 |
| Sufficient Dimension Reduction in Regression | |
| Sufficient Dimension Reduction in Regression | p. 257 |
| Introduction | p. 257 |
| Sufficient dimension reduction in regression | p. 258 |
| Sufficient variable selection (SVS) | p. 265 |
| SDR for correlated data and large-p-small-n | p. 266 |
| Further discussion | p. 267 |
| References | p. 268 |
| Combining Statistical Procedures | p. 275 |
| Introduction | p. 275 |
| Combining for adaptation | p. 279 |
| Combining procedures for improvement | p. 288 |
| Concluding remarks | p. 294 |
| References | p. 295 |
| Subject Index | p. 299 |
| Author Index | p. 301 |
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