Comment: Settle the Unsettling: An Inferential Models Perspective

被引：2

作者：

Liu, Chuanhai ^{[1
]}

Martin, Ryan ^{[2
]}

机构：

[1] Purdue Univ, Dept Stat, W Lafayette, IN 47907 USA

[2] North Carolina State Univ, Dept Stat, Raleigh, NC USA

来源：

STATISTICAL SCIENCE | 2021年 / 36卷 / 02期

关键词：

Belief function; efficiency; lower and upper probability; inferential models; validity;

D O I：

10.1214/21-STS765B

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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."

引用

页码：196 / 200

页数：5

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