A More Robust t-Test

• 成果类型: Article
• 署名作者: Mueller, Ulrich K.
• 署名单位: Princeton University
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest_a_01291
• 发表日期: 2025-05
• 页码: 786-802
• 关键词: inference; CONVERGENCE

摘要

The paper combines extreme value theory for the smallest and largest k observations for some given k > 1 with a normal approximation for the average of the remaining observations to construct a more robust alternative to the usual t-test. The new test is found to control size much more successfully in small samples compared to existing methods. This holds for the canonical inference for the mean problem based on an i.i.d. sample, but also when comparing two population means and when conducting inference about linear regression coefficients with clustered standard errors.