1st Edition
Sparse Graphical Modeling for High Dimensional Data A Paradigm of Conditional Independence Tests
This book provides a general framework for learning sparse graphical models with conditional independence tests. It includes complete treatments for Gaussian, Poisson, multinomial, and mixed data; unified treatments for covariate adjustments, data integration, and network comparison; unified treatments for missing data and heterogeneous data; efficient methods for joint estimation of multiple graphical models; effective methods of high-dimensional variable selection; and effective methods of high-dimensional inference. The methods possess an embarrassingly parallel structure in performing conditional independence tests, and the computation can be significantly accelerated by running in parallel on a multi-core computer or a parallel architecture. This book is intended to serve researchers and scientists interested in high-dimensional statistics, and graduate students in broad data science disciplines.
Key Features:
- A general framework for learning sparse graphical models with conditional independence tests
- Complete treatments for different types of data, Gaussian, Poisson, multinomial, and mixed data
- Unified treatments for data integration, network comparison, and covariate adjustment
- Unified treatments for missing data and heterogeneous data
- Efficient methods for joint estimation of multiple graphical models
- Effective methods of high-dimensional variable selection
- Effective methods of high-dimensional inference
1. Introduction to Sparse Graphical Models 2. Gaussian Graphical Models 3. Gaussian Graphical Modeling with Missing Data 4. Gaussian Graphical Modeling for Heterogeneous Data 5. Poisson Graphical Models 6. Mixed Graphical Models 7. Joint Estimation of Multiple Graphical Models 8. Nonlinear and Non-Gaussian Graphical Models 9. High-Dimensional Inference with the Aid of Sparse Graphical Modeling 10. Appendix
Biography
Dr. Faming Liang is Distinguished Professor of Statistics, Purdue University. Prior joining Purdue University in 2017, he held regular faculty positions in the Department of Biostatistics, University of Florida and Department of Statistics, Texas A&M University. Dr. Liang obtained his PhD degree from the Chinese University of Hong Kong in 1997. Dr. Liang is ASA fellow, IMS fellow, and elected member of International Statistical Association. Dr. Liang is also a winner of Youden Prize 2017. Dr. Liang has served as co-editor for Journal of Computational and Graphical Statistics, associate editor for multiple statistical journals, including Journal of the American Statistical Association, Journal of Computational and Graphical Statistics, Technometrics, Bayesian Analysis, and Biometrics, and editorial board member for Nature Scientific Report. Dr. Liang has published two books and over 130 journal/conference papers, which involve a variety of research fields such as Markov chain Monte Carlo, machine learning, bioinformatics, high-dimensional statistics, and big data computing.
Dr. Bochao Jia is research scientist at Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana, U.S.A. Dr. Jia obtained his PhD degree from University of Florida in 2018. Dr. Jia has published quite a few papers on sparse graphical modelling.
