Sparse Graphical Modeling for High Dimensional Data
A Paradigm of Conditional Independence Tests
Faming Liang author Bochao Jia author
Format:Hardback
Publisher:Taylor & Francis Ltd
Published:2nd Aug '23
Should be back in stock very soon
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 <
"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.
ISBN: 9780367183738
Dimensions: unknown
Weight: 780g
130 pages