Catalytic prior distributions with application to generalized linear models
Abstract
We propose a strategy for building prior distributions that stabilize the estimation of complex “working models” when sample sizes are too small for standard statistical analysis. The stabilization is achieved by supplementing the observed data with a small amount of synthetic data generated from the predictive distribution of a simpler model. This class of prior distributions is easy to use and allows direct statistical interpretation.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 15, 2020
- Source ID
- 10.1073/pnas.1920913117
Entities
People
- Donald B. Rubin
- Dongming Huang
- Nathan Stein
- S. C. Kou
Organizations
- Division of Information and Intelligent Systems
- Harvard University
- National Institute of General Medical Sciences
- National Science Foundation Division of Mathematical Sciences
- Office of Naval Research
- Temple University
- Tsinghua University
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