JSM 2005 - Toronto

Abstract #302957

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Activity Number: 51
Type: Invited
Date/Time: Sunday, August 7, 2005 : 4:00 PM to 5:50 PM
Sponsor: General Methodology
Abstract - #302957
Title: Saddlepoint Approximations to Distribution Functions
Author(s): John Kolassa*+
Companies: Rutgers University
Address: 110 Frelinghuysen Road, Piscataway, NJ, 07924,
Keywords: Saddlepoint approximation
Abstract:

This paper presents a saddlepoint approximation to the cumulative distribution function of a random vector. The proposed approximation has accuracy comparable to that of existing expansions valid in two dimensions and may be applied to random vectors of arbitrary length---subject only to the requirement that the distribution approximated either have a density or be confined to a lattice and have a cumulant generating function. The result is derived by directly inverting the multivariate moment generating function. The result is applied to sufficient statistics from a regression model with exponential errors, an existing method in two dimensions, and a multivariate inference from a dataset arising from a case-control study of endometrial cancer.

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