Munich Personal RePEc Archive
metadata only
On the Effect of Prior Assumptions in Bayesian Model Averaging with Applications to Growth Regression
We consider the problem of variable selection in linear regression models. Bayesian model averaging has become an important tool in empirical settings with large numbers of potential regressors and relatively limited numbers of observations. We examine the effect of a variety of prior assumptions on
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- An Effect of Violating Independence Assumption between parameters in Analyses for the Estimation of Mean, Correlation, and Linear regression Using Bayesian Hierarchical Modeling#223250
- AdaPS-LiNGAM: Adaptive Predecessor Selection for Linear Non-Gaussian Acyclic Models under Small-Sample Settings#1234149
- Estimation Associated with Linear Models: A Revisitation#1149659
- LinearPFN: Amortized Variable Selection for Linear Models with Interactions#1215678
- Objective Model Prior Probabilities in Variable Selection#1035431
Record · ID 1277166
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive