Sparse Graphical Modeling for High Dimensional Data

A Paradigm of Conditional Independence Tests

By: Faming Liang, Bochao Jia

Write A Review

eText | 2 August 2023 | Edition Number 1

At a Glance

Format
ePUB

eText

$118.80

or 4 interest-free payments of $29.70 with

Instant online reading in your Booktopia eTextbook Library *

Why choose an eTextbook?

Instant Access *

Purchase and read your book immediately

Read Aloud

Listen and follow along as Bookshelf reads to you

Study Tools

Built-in study tools like highlights and more

* eTextbooks are not downloadable to your eReader or an app and can be accessed via web browsers only. You must be connected to the internet and have no technical issues with your device or browser that could prevent the eTextbook from operating.

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