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Abstract

The modern theory of likelihood inference provides improved inferences in many parametric models, with little more effort than is required for application of standard first-order theory. We outline the relevant computations, and illustrate the calculations using a dilution assay, a zero-inflated Poisson regression model, and a short time series. In each case the effect of the higher order correction can be appreciable.

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