On Graphical Models and Convex Geometry
2021
We introduce a mixture-model of beta distributions to identify significant correlations among $P$ predictors when $P$ is large. The method relies on theorems in convex geometry, which we use to show how to control the error rate of edge detection in graphical models. Our `betaMix' method does not require any assumptions about the network structure, nor does it assume that the network is sparse. The results in this article hold for a wide class of data generating distributions that include light-tailed and heavy-tailed spherically symmetric distributions.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
44
References
0
Citations
NaN
KQI