Learning Statistics From Counterexamples

被引：1
|
作者：
Berger, James [1 ]
机构：
[1] Duke Univ, Durham, NC 27708 USA
来源：
SANKHYA-SERIES A-MATHEMATICAL STATISTICS AND PROBABILITY | 2024年 / 86卷 / SUPPL 1期
关键词：
Horvitz-Thompson estimator; ancillarity; likelihood principle; stopping rule principle; conditioning; shrinkage estimation; Jeffreys-Lindley paradox; p-values and error rates; understanding p-values; underestimating variances in elicitation; difficulties with conjugate priors; Neyman-Scott problem; difficulties with the multivariate Jeffreys prior; empirical Bayes counterexample; justifying improper priors; multinomial counterexample; Bartlett counterexample; median probability model; epistemic and aleatoric probability; robust Bayesian analysis; imprecise probability; FOUNDATIONS; INFERENCE;
D O I：
10.1007/s13171-024-00356-8
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：
The title of this article is (essentially) the same as the famous paper Basu (2011b). Basu often opined that counterexamples were the best way to learn limitations of theories or methods and I have followed his directive in my own teaching. A number of counterexamples I use extensively in teaching are collected here.
引用
收藏
页码：13 / 42
页数：30
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