A New Look at $F$-Tests

Abstract

Directional inference for vector parameters based on higher order approximations in likelihood inference has recently been developed in the literature. Here we explore examples of directional inference where the calculations can be simplified. It is found that in several classical situations the directional test is equivalent to the usual $F$-test. These situations include testing the ratio of group variances and rates from normal and exponential distributions, linear regression with a linear constraint and Hotelling's $T^2$-test.