REVIEW 1 cited by
Bayesian Selective Inference: Non-informative Priors
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We discuss Bayesian inference for parameters selected using the data. First, we provide a critical analysis of the existing positions in the literature regarding the correct Bayesian approach under selection. Second, we propose two types of non-informative priors for selection models. These priors may be employed to produce a posterior distribution in the absence of prior information as well as to provide well-calibrated frequentist inference for the selected parameter. We test the proposed priors empirically in several scenarios.
Forward citations
Cited by 1 Pith paper
-
Sampling models for selective inference
The paper proves that two forms of ancillarity are preserved under conditioning on a selection event and uses this to guide sampling-model choice in selective inference.
Discussion (0). Continue with ORCID to comment.