Plausibility Functions and Exact Frequentist Inference

Ryan Martin

Journal of the American Statistical Association, 2015, vol. 110, issue 512, 1552-1561

Abstract: 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.

Date: 2015
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DOI: 10.1080/01621459.2014.983232

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