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Sparse graphical modeling for high dimensional data : a paradigm of conditional independence tests / Faming Liang, Bochao Jia
Mitwirkende(r): Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: Chapman & Hall/CRC monographs on statistics & applied probabilityVerlag: Boca Raton : CRC Press, 2020Beschreibung: 1 Online-Ressource : illustrationsISBN:- 9780429584800
- 0429584806
- 9780367183738
- 519.5/38 23/eng/20230728
- QA279
Inhalte:
Zusammenfassung: "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"--PPN: PPN: 1882648196Package identifier: Produktsigel: ZDB-4-NLEBK | BSZ-4-NLEBK-KAUB
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