Big and complex data analysis, contributions to statistics

✍ Scribed by Ahmed S.E. (ed.)

• Publisher: Springer
• Year: 2017
• Tongue: English
• Leaves: 390
• Category: Library

✦ Synopsis

This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential new developments in the field.

The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. Simultaneous variable selection and estimation is one of the key statistical problems involved in analyzing such big and complex data.

The purpose of this book is to stimulate research and foster interaction between researchers in the area of high-dimensional data analysis. More concretely, its goals are to: 1) highlight and expand the breadth of existing methods in big data and high-dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2) identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodologies for different application areas; and 3) facilitate collaboration between theoretical and subject-specific researchers.

✦ Table of Contents

Front Matter....Pages i-xiv
Front Matter....Pages 1-1
Regularization After Marginal Learning for Ultra-High Dimensional Regression Models....Pages 3-28
Empirical Likelihood Test for High Dimensional Generalized Linear Models....Pages 29-50
Random Projections for Large-Scale Regression....Pages 51-68
Testing in the Presence of Nuisance Parameters: Some Comments on Tests Post-Model-Selection and Random Critical Values....Pages 69-82
Analysis of Correlated Data with Error-Prone Response Under Generalized Linear Mixed Models....Pages 83-102
Bias-Reduced Moment Estimators of Population Spectral Distribution and Their Applications....Pages 103-119
Front Matter....Pages 121-121
Statistical Process Control Charts as a Tool for Analyzing Big Data....Pages 123-138
Fast Community Detection in Complex Networks with a K-Depths Classifier....Pages 139-157
How Different Are Estimated Genetic Networks of Cancer Subtypes?....Pages 159-192
A Computationally Efficient Approach for Modeling Complex and Big Survival Data....Pages 193-207
Tests of Concentration for Low-Dimensional and High-Dimensional Directional Data....Pages 209-227
Nonparametric Testing for Heterogeneous Correlation....Pages 229-246
Front Matter....Pages 247-247
Optimal Shrinkage Estimation in Heteroscedastic Hierarchical Linear Models....Pages 249-284
High Dimensional Data Analysis: Integrating Submodels....Pages 285-304
High-Dimensional Classification for Brain Decoding....Pages 305-324
Unsupervised Bump Hunting Using Principal Components....Pages 325-345
Identifying Gene–Environment Interactions Associated with Prognosis Using Penalized Quantile Regression....Pages 347-367
A Mixture of Variance-Gamma Factor Analyzers....Pages 369-385