Sparse Graphical Modeling for High Dimensional Data: A Paradigm of Conditional Independence Tests

This book provides a comprehensive framework for learning sparse graphical models with conditional independence tests, covering various data types, adjustments, and inference methods. It offers efficient parallel computing for accelerated analysis, making it valuable for researchers and students in high-dimensional statistics and data science.

Format: Hardback
Length: 130 pages
Publication date: 02 August 2023
Publisher: Taylor & Francis Ltd

Target Audience: Researchers and scientists interested in high-dimensional statistics Graduate students in broad data science disciplines
This comprehensive book offers a thorough and insightful approach to learning sparse graphical models with conditional independence tests. It provides comprehensive coverage of various data types, including Gaussian, Poisson, multinomial, and mixed data, ensuring a unified treatment for diverse statistical scenarios. The book emphasizes the importance of covariate adjustments, data integration, and network comparison, offering unified methods for addressing these challenges. Moreover, it addresses the complexities of missing data and heterogeneous data, presenting efficient techniques for handling such data.

In terms of estimation, the book introduces powerful methods for jointly estimating multiple graphical models, leveraging the advantages of parallel computing to significantly speed up the computation process. This parallel structure enables researchers and scientists to efficiently analyze large datasets and uncover valuable insights.

Furthermore, the book delves into effective methods of high-dimensional variable selection, aiding in the identification of important variables that contribute to the model's performance. This feature is particularly valuable in high-dimensional data analysis, where the number of variables can be overwhelming.

The book concludes with comprehensive discussions on high-dimensional inference, providing insights into the statistical principles and methodologies employed to make predictions and draw conclusions based on the learned graphical models.

Designed for researchers and scientists engaged in high-dimensional statistics and graduate students in broad data science disciplines, this book serves as a valuable resource for advancing the understanding and application of sparse graphical models in modern data analysis.

Weight: 348g
Dimension: 162 x 242 x 14 (mm)
ISBN-13: 9780367183738