Arbeitspapier
Graphical model inference with external network data
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: cemmap working paper ; No. CWP20/22
- Klassifikation
-
Wirtschaft
- Thema
-
GLASSO
Bayesian Inference
Spike-and-Slab
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Jewson, Jack
Li, Li
Battaglia, Laura
Hansen, Stephen
Rossell, David
Zwiernik, Piotr
- Ereignis
-
Veröffentlichung
- (wer)
-
Centre for Microdata Methods and Practice (cemmap)
- (wo)
-
London
- (wann)
-
2022
- DOI
-
doi:10.47004/wp.cem.2022.2022
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Jewson, Jack
- Li, Li
- Battaglia, Laura
- Hansen, Stephen
- Rossell, David
- Zwiernik, Piotr
- Centre for Microdata Methods and Practice (cemmap)
Entstanden
- 2022
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