Lozada-Can, C.Davison, A. C.2010-02-242010-02-242010-02-24201010.1198/tast.2010.09004https://infoscience.epfl.ch/handle/20.500.14299/47683WOS:000278250500005The 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.AutoregressionBias reductionDilution assayHigher order asymptoticsLikelihoodZero-inflated Poisson distributionConditional InferenceAsymptoticsRatiotext::journal::journal article::research article