LR-GLM: High-dimensional Bayesian inference using low-rank data approximations
Due to the ease of modern data collection, applied statisticians often have access to a large set of covariates that they wish to relate to some observed outcome. Generalized linear models (GLMs) offer a particularly interpretable framework for such an analysis. In these high-dimensional problems, t...
| Main Authors: | , , |
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| Other Authors: | , |
| Format: | Article |
| Language: | English |
| Published: |
MIT Press,
2020-12-10T16:46:43Z.
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| Subjects: | |
| Online Access: | Get fulltext |