Approximate Conditional Inference in Exponential Families Via the Gibbs Sampler

DOI10.2307/2290874zbMath0803.62013OpenAlexW4240536253MaRDI QIDQ4305744

Martin A. Tanner, John E. Kolassa

Publication date: 15 September 1994

Full work available at URL: https://doi.org/10.2307/2290874

zbMATH Keywords

nuisance parametersMarkov chainGibbs samplersufficient statisticsPoisson regressionequilibrium distributionconditional inferenceconditional cumulative distribution functiondouble saddlepoint approximationapproximate frequentist inferenceGibbs-Skovgaard algorithm

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Inference from stochastic processes (62M99) Contingency tables (62H17)

Related Items (13)

Comparing three partitions: an inferential approach based on multi-way contingency tables ⋮ Markov chains for Monte Carlo tests of genetic equilibrium in multidimensional contingency tables ⋮ Higher-order approximations to conditional distribution functions ⋮ Uniformity of double saddlepoint conditional probability approximations ⋮ Nearly exact tests of conditional independence and marginal homogeneity for sparse contingency tables ⋮ Asymptotics and the theory of inference ⋮ Approximating the distribution function of risk ⋮ A Markov chain sampler for contingency table exact inference ⋮ Small‐Sample Confidence Regions in Exponential Families ⋮ Algebraic algorithms for sampling from conditional distributions ⋮ Convergence and accuracy of Gibbs sampling for conditional distributions in generalized linear models ⋮ Likelihood and higher‐order approximations to tail areas: A review and annotated bibliography ⋮ Representations, bounds and approximations for tail probabilites of multivariate non-central hypergeometric and negative hypergeometric distributions

Uses Software

• AS 159

This page was built for publication: Approximate Conditional Inference in Exponential Families Via the Gibbs Sampler