Big and Complex Data Analysis: Methodologies and Applications

Preface.- Introduction.- Unsupervised Bump Hunting Using Principal Components.- Statistical Process Control Charts as a Tool for Analyzing Big Data.- Empirical Likelihood Test for High Dimensional Generalized Linear Models.- Identifying gene-environment interactions associated with prognosis using penalized quantile regression.- A Computationally Efficient Approach for Modeling Complex and Big Survival Data.- Regularization after marginal learning for ultra-high dimensional regression models.- Tests of concentration for low-dimensional and high-dimensional directional data.- Random Projections For Large-Scale Regression.- How Different are Estimated Genetic Networks of Cancer Subtypes?.- Analysis of correlated data with error-prone response under generalized linear mixed models.- High-Dimensional Classification for Brain Decoding.- Optimal shrinkage estimation in heteroscedastic hierarchical linear models.- Bias-reduced moment estimators of Population Spectral Distribution and their applications.- Testing in the Presence of Nuisance Parameters: Some Comments on Tests Post-Model-Selection and Random Critical Values.- A Mixture of Variance-Gamma Factor Analyzers.- Fast Community Detection in Complex Networks with a K-Depths Classifier.