 Saddlepoint density approximation

For independent identically distributed continuous random variables, let $K(t)=\log \mathbb{E}{e}^{tY}$ be finite near zero. At a mean value $x$ reached by $K^{\prime }(\hat{t})=x$ in the interior of that domain, the displayed leading approximation describes the density of the sum $S_n$. Exponential tilting by $\hat{t}$ puts the sum at its tilted mean; a local normal approximation gives the denominator, and undoing the tilt gives the exponential factor. Under suitable smooth nonlattice regularity, at fixed interior $x$ the exact density is the displayed quantity times $1+O(n^{-1})$. This statistical approximation is an application of the saddle-point approximation to a transform inversion integral. It requires more than the existence of a few ordinary moments.