2012/03/31 by Ryan Martin · 39 citations
Computer Science · Decision Sciences · Mathematics · #Advanced Statistical Methods and Models #Advanced Statistical Process Monitoring #Applied mathematics #Artificial intelligence #Bayesian Modeling and Causal Inference #Bayesian inference #Bayesian probability #Computer science #Econometrics #Frequentist inference #Inference #Mathematics #Statistical inference #Statistics #math.ST #stat.ME #stat.TH
paper · pdf · doi:10.1080/01621459.2014.983232
published in Journal of the American Statistical Association 110(512), 1552-1561 · 21 pages, 5 figures, 3 tables
arxiv created 2014/03/12 · openalex publication_date 2014/11/15 · arxiv updated 2016/01/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
In the frequentist program, inferential methods with exact control on error rates are a primary focus. The standard approach, however, is to rely on asymptotic approximations, which may not be suitable. This article presents a general framework for the construction of exact frequentist procedures based on plausibility functions. It is shown that the plausibility function-based tests and confidence regions have the desired frequentist properties in finite samples—no large-sample justification needed. An extension of the proposed method is also given for problems involving nuisance parameters. Examples demonstrate that the plausibility function-based method is both exact and efficient in a wide variety of problems.