Partial Conditioning for Inference of Many-Normal-Means with Hölder Constraints
arxiv(2023)
摘要
Inferential models have been proposed for valid and efficient prior-free
probabilistic inference. As it gradually gained popularity, this theory is
subject to further developments for practically challenging problems. This
paper considers the many-normal-means problem with the means constrained to be
in the neighborhood of each other, formally represented by a Hölder space. A
new method, called partial conditioning, is proposed to generate valid and
efficient marginal inference about the individual means. It is shown that the
method outperforms both a fiducial-counterpart in terms of validity and a
conservative-counterpart in terms of efficiency. We conclude the paper by
remarking that a general theory of partial conditioning for inferential models
deserves future development.
更多查看译文
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要