Saddlepoint approximations as smoothers

Abstract

This note investigates the sense in which saddlepoint approximations act as smoothers of discrete distributions. The discrete problem is embedded in a continuous model that closely matches it on the discrete sample space, with saddlepoint approximation yielding an inference that is almost exact for the continuous model. The same applies to conditional distributions. An example is given and implications for inference are discussed.