Views on conditional and marginal methods of statistical inference
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Conditional and marginal likelihood analysis has a long history of development. Some recent methods using exact and approximate density and distribution functions lead to more sharply defined likelihoods and to accurate observed levels of significance for a wide range of problems including nonnormal regression and exponential linear models. These developments will be surveyed.