Various randomization distributions are shown to arise as conditional distributions in the setting of generalized linear models. These conditional distributions admit saddlepoint approximations which can obviate the need to simulate the randomization distributions they approximate. Examples include multinomial and Dirichlet bootstraps, the jackknife, and permutation distributions. The double saddlepoint approximation in a regular exponential family is shown to be equivalent to conditional use of a single saddlepoint approximation when the exponential family admits to a cut
conditional inference; Gram-Charlier approximations; importance sampling. This paper considers a cla...
We present two techniques for constructing sample spaces that approximate probability distributions....
AbstractBarndorff-Nielsen's formula (normed likelihood with constant-information metric) has been pr...
This paper presents results showing that the error involved in using the double saddlepoint distribu...
Saddlepoint methods present a convenient way to approximate probabilities associated with canonical ...
Thesis (Ph. D.)--University of Washington, 1996Higher order asymptotic methods based on the saddlepo...
AbstractThis paper presents results showing that the error involved in using the double saddlepoint ...
A saddlepoint approximation is provided for the distribution function of one M statistic conditional...
This paper derives the saddlepoint approximation for a linear combination of the convolution , wher...
Saddlepoint approximations of marginal densities and tail probabilities of general nonlinear statist...
Saddlepoint approximations of marginal densities and tail probabilities of general nonlinear statist...
Saddlepoint approximations are extended to general statistics. The technique is applied to derive ap...
We extend known saddlepoint tail probability approximations to multivariate cases, including multiva...
We discuss some recent (nonparametric) approximations of tail probabilities of marginal distribution...
This note investigates the sense in which saddlepoint approximations act as smoothers of discrete di...
conditional inference; Gram-Charlier approximations; importance sampling. This paper considers a cla...
We present two techniques for constructing sample spaces that approximate probability distributions....
AbstractBarndorff-Nielsen's formula (normed likelihood with constant-information metric) has been pr...
This paper presents results showing that the error involved in using the double saddlepoint distribu...
Saddlepoint methods present a convenient way to approximate probabilities associated with canonical ...
Thesis (Ph. D.)--University of Washington, 1996Higher order asymptotic methods based on the saddlepo...
AbstractThis paper presents results showing that the error involved in using the double saddlepoint ...
A saddlepoint approximation is provided for the distribution function of one M statistic conditional...
This paper derives the saddlepoint approximation for a linear combination of the convolution , wher...
Saddlepoint approximations of marginal densities and tail probabilities of general nonlinear statist...
Saddlepoint approximations of marginal densities and tail probabilities of general nonlinear statist...
Saddlepoint approximations are extended to general statistics. The technique is applied to derive ap...
We extend known saddlepoint tail probability approximations to multivariate cases, including multiva...
We discuss some recent (nonparametric) approximations of tail probabilities of marginal distribution...
This note investigates the sense in which saddlepoint approximations act as smoothers of discrete di...
conditional inference; Gram-Charlier approximations; importance sampling. This paper considers a cla...
We present two techniques for constructing sample spaces that approximate probability distributions....
AbstractBarndorff-Nielsen's formula (normed likelihood with constant-information metric) has been pr...