Saddlepoint approximation method

If the moment generating function of a distribution is written as ${\displaystyle M(t)}$ and the cumulant generating function as ${\displaystyle K(t)=\log(M(t))}$ then the saddlepoint approximation to the PDF of a distribution is defined as:

${\displaystyle {\hat {f}}(x)={\frac {1}{\sqrt {2\pi K''({\hat {s}})}}}\exp(K({\hat {s}})-{\hat {s}}x)}$

and the saddlepoint approximation to the CDF is defined as:

${\displaystyle {\hat {F}}(x)={\begin{cases}\Phi ({\hat {w}})+\phi ({\hat {w}})({\frac {1}{\hat {w}}}-{\frac {1}{\hat {u}}})&{\text{for }}x\neq \mu \\{\frac {1}{2}}+{\frac {K'''(0)}{6{\sqrt {2\pi }}K''(0)^{3/2}}}&{\text{for }}x=\mu \end{cases}}}$

where ${\displaystyle {\hat {s}}}$ is the solution to ${\displaystyle K'({\hat {s}})=x}$, ${\displaystyle {\hat {w}}=\operatorname {sgn} {\hat {s}}{\sqrt {2({\hat {s}}x-K({\hat {s}}))}}}$ and ${\displaystyle {\hat {u}}={\hat {s}}{\sqrt {K''({\hat {s}})}}}$

• Butler, Ronald W. (2007), Saddlepoint approximations with applications, Cambridge: Cambridge University Press, ISBN 9780521872508
• Daniels, H. E. (1954), "Saddlepoint Approximations in Statistics", The Annals of Mathematical Statistics, 25 (4): 631–650, doi:10.1214/aoms/1177728652
• Daniels, H. E. (1980), "Exact Saddlepoint Approximations", Biometrika, 67 (1): 59–63, doi:10.1093/biomet/67.1.59, JSTOR 2335316
• Lugannani, R.; Rice, S. (1980), "Saddle Point Approximation for the Distribution of the Sum of Independent Random Variables", Advances in Applied Probability, 12 (2): 475–490, doi:10.2307/1426607, JSTOR 1426607