Sparse Graphical Modeling for High Dimensional Data

Sparse Graphical Modeling for High Dimensional Data A Paradigm of Conditional Independence Tests - Chapman & Hall/CRC Monographs on Statistics & Applied Probability

Hardback (02 Aug 2023)

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Publisher's Synopsis

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

Book information

ISBN: 9780367183738
Publisher: CRC Press
Imprint: Chapman & Hall/CRC
Pub date:
DEWEY: 519.5
DEWEY edition: 23
Language: English
Number of pages: 130
Weight: 348g
Height: 162mm
Width: 242mm
Spine width: 14mm