Estimation of Graphical Models: An Overview of Selected Topics

Summary Graphical modelling is an important branch of statistics that has been successfully applied in biology, social science, causal inference and so on. Graphical models illuminate connections between many variables and can even describe complex data structures or noisy data. Graphical models hav...

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Bibliographic Details
Published in:International statistical review 2024-08, Vol.92 (2), p.194-245
Main Author: Chen, Li‐Pang
Format: Article
Language:English
Subjects:
Algorithms
Bayesian analysis
Classification
complex and noisy data
Complex variables
computational algorithm
conditional inference
Data structures
graphical LASSO
graphical models
Machine learning
multivariate linear models
network structure
Nonparametric statistics
optimisation
pairwise dependence
Statistical inference
Supervised learning
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Summary:Summary Graphical modelling is an important branch of statistics that has been successfully applied in biology, social science, causal inference and so on. Graphical models illuminate connections between many variables and can even describe complex data structures or noisy data. Graphical models have been combined with supervised learning techniques such as regression modelling and classification analysis with multi‐class responses. This paper first reviews some fundamental graphical modelling concepts, focusing on estimation methods and computational algorithms. Several advanced topics are then considered, delving into complex graphical structures and noisy data. Applications in regression and classification are considered throughout.
ISSN:0306-7734
1751-5823
DOI:10.1111/insr.12552