Sparse Graphical Modeling for High Dimensional Data
A Paradigm of Conditional Independence Tests
1st Edition
Published on 2. August 2023
150 pages
978-0-429-58480-0 (ISBN)
Description
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
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
Reviews / Votes
"This book is highly recommended for statistical researchers working in high-dimensional graphical modeling, data scientists, graduate students, and graduates in statistics, biostatistics, biology, computing, or various disciplines. This book provides readers with an in-depth understanding of various methods and techniques in modern data analysis, especially in mixed data, high-dimensional data, and graphical models."Vira Ananda, Institut Teknologi Bandung, Indonesia, Technometrics, May 2024.
"Consider this book not merely as a manual but as a gateway to mastering the art and science of sparse graphical modeling. It stands ready to serve as both a seasoned guide for professionals and an enlightening companion for students. In a field increasingly recognized for its critical importance, this text shines as a beacon, guiding beginners and applied scientists alike."
Reza Mohammadi, University of Amsterdam, Netherlands, Journal of the American Statistical Association, July 2024.
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Illustrations
12 Tables, black and white; 8 Line drawings, color; 7 Line drawings, black and white; 8 Illustrations, color; 7 Illustrations, black and white
ISBN-13
978-0-429-58480-0 (9780429584800)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Faming Liang | Bochao Jia
Sparse Graphical Modeling for High Dimensional Data
A Paradigm of Conditional Independence Tests
Book
08/2023
1st Edition
Chapman & Hall/CRC
€131.80
Shipment within 15-20 days
Persons
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.
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.
Content
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
