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Februrary 2003 Multivariate saddlepoint tail probability approximations
John E. Kolassa
Ann. Statist. 31(1): 274-286 (Februrary 2003). DOI: 10.1214/aos/1046294465


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 twodimensions, 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, and compared to an existing method in two dimensions. The result is also applied to multivariate inference from a data set arising from a case-control study of endometrial cancer.


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John E. Kolassa. "Multivariate saddlepoint tail probability approximations." Ann. Statist. 31 (1) 274 - 286, Februrary 2003.


Published: Februrary 2003
First available in Project Euclid: 26 February 2003

zbMATH: 1018.62012
MathSciNet: MR1962507
Digital Object Identifier: 10.1214/aos/1046294465

Primary: 62E20
Secondary: 60E99

Keywords: conditional probability , hypergeometric distribution , lattice variable , tail probability

Rights: Copyright © 2003 Institute of Mathematical Statistics


Vol.31 • No. 1 • Februrary 2003
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