Approximate Monte Carlo conditional inference in exponential families.

Abstract

This article presents an algorithm for approximate frequentist conditional inference on two or more parameters for any regression model in the Generalized Linear Model (GLIM) family. We thereby extend highly accurate inference beyond the cases of logistic regression and contingency tables implimented in commercially available software. The method makes use… (More)

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Cite this paper

@article{Kolassa1999ApproximateMC, title={Approximate Monte Carlo conditional inference in exponential families.}, author={John Kolassa and Martin A. Tanner}, journal={Biometrics}, year={1999}, volume={55 1}, pages={246-51} }