Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/272832 
Year of Publication: 
2022
Series/Report no.: 
cemmap working paper No. CWP20/22
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
A frequent challenge when using graphical models in applications is that the sample size is limited relative to the number of parameters to be learned. Our motivation stems from applications where one has external data, in the form of networks between variables, that provides valuable information to help improve inference. Specifically, we depict the relation between COVID-19 cases and social and geographical network data, and between stock market returns and economic and policy networks extracted from text data. We propose a graphical LASSO framework where likelihood penalties are guided by the external network data. We also propose a spike-and-slab prior framework that depicts how partial correlations depend on the networks, which helps interpret the fitted graphical model and its relationship to the network. We develop computational schemes and software implementations in R and probabilistic programming languages. Our applications show how incorporating network data can significantly improve interpretation, statistical accuracy, and out-of-sample prediction, in some instances using significantly sparser graphical models than would have otherwise been estimated.
Subjects: 
GLASSO
Bayesian Inference
Spike-and-Slab
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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