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

Sparse Graphical Modeling for High Dimensional Data cover

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
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"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