Approximate Conditional Inference in Logistic and Loglinear Models

Abstract

Recently developed small-sample asymptotics provide nearly exact inference for parametric statistical models. One approach is via approximate conditional and marginal inference, respectively, in multiparameter exponential families and regression-scale models. Although the theory is well developed, these methods are under-used in practical work. This article presents a set of S-Plus routines for approximate conditional inference in logistic and loglinear regression models. It represents the first step of a project to create a library for small-sample inference which will include methods for some of the most widely used statistical models. Details of how the methods have been implemented are discussed. An example illustrates the code.