On the robustness of analytical and bootstrap corrections to score tests in regression models
Two different approaches to obtaining finite-sample corrections to score tests are the analytical and the computational approaches. The former is based either on a Bartlett-type correction to the test statistic or on the inversion of an Edgeworth expansion to its null distribution. The latter, on the other hand, is usually based on a bootstrapping resampling scheme. This paper provides a numerical comparison of the size and power properties of these two approaches both under correct model specification and under model misspecification.
robustness bootstrap score test edgeworth expansion bartlett correction nonlinear regression