  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 paper 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.
