Multivariate saddlepoint approximations in tail probability and conditional inference
Statistics Theory
2010-11-29 v1 Statistics Theory
Abstract
We extend known saddlepoint tail probability approximations to multivariate cases, including multivariate conditional cases. Our approximation applies to both continuous and lattice variables, and requires the existence of a cumulant generating function. The method is applied to some examples, including a real data set from a case-control study of endometrial cancer. The method contains less terms and is easier to implement than existing methods, while showing an accuracy comparable to those methods.
Cite
@article{arxiv.1011.5775,
title = {Multivariate saddlepoint approximations in tail probability and conditional inference},
author = {John Kolassa and Jixin Li},
journal= {arXiv preprint arXiv:1011.5775},
year = {2010}
}
Comments
Published in at http://dx.doi.org/10.3150/09-BEJ237 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)