评论：解决不安：一个推理模型的视角

IF 3.9 1区数学 Q1 STATISTICS & PROBABILITY

Statistical Science Pub Date : 2021-05-01 DOI:10.1214/21-STS765B

Chuanhai Liu, Ryan Martin

引用次数: 8

摘要

在这里，我们证明了推理模型（IM）框架与龚和孟所展示的不可靠的更新规则不同，它提供了有效的推断/预测，同时不易受到确定性损失的影响。从这个意义上说，IM框架解决了龚和孟所说的“令人不安”

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Comment: Settle the Unsettling: An Inferential Models Perspective

Here, we demonstrate that the inferential model (IM) framework, unlike the updating rules that Gong and Meng show to be unreliable, provides valid and efficient inferences/prediction while not being susceptible to sure loss. In this sense, the IM framework settles what Gong and Meng characterized as “unsettling.”

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来源期刊

Statistical Science 数学-统计学与概率论

CiteScore

6.50

自引率

1.80%

发文量

审稿时长

>12 weeks

期刊介绍： The central purpose of Statistical Science is to convey the richness, breadth and unity of the field by presenting the full range of contemporary statistical thought at a moderate technical level, accessible to the wide community of practitioners, researchers and students of statistics and probability.