Infinite Diameter Confidence Sets in Hedges' Publication Bias Model
Statistics Theory
2022-04-01 v3 Statistics Theory
Abstract
Meta-analysis, the statistical analysis of results from separate studies, is a fundamental building block of science. But the assumptions of classical meta-analysis models are not satisfied whenever publication bias is present, which causes inconsistent parameter estimates. Hedges' selection function model takes publication bias into account, but estimating and inferring with this model is tough for some datasets. Using a generalized Gleser-Hwang theorem, we show there is no confidence set of guaranteed finite diameter for the parameters of Hedges' selection model. This result provides a partial explanation for why inference with Hedges' selection model is fraught with difficulties.
Keywords
Cite
@article{arxiv.1912.09180,
title = {Infinite Diameter Confidence Sets in Hedges' Publication Bias Model},
author = {Jonas Moss},
journal= {arXiv preprint arXiv:1912.09180},
year = {2022}
}
Comments
12 pages