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Views on conditional and marginal methods of statistical inference

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Abstract

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.

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  1. Department of Mathematics, York University, 4700 Keele Street, M3J 1P3, North York, Ontario, Canada

    D. A. S. Fraser

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  1. D. A. S. Fraser
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Fraser, D.A.S. Views on conditional and marginal methods of statistical inference. Statistical Papers 31, 83–93 (1990). https://doi.org/10.1007/BF02924678

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  • DOI: https://doi.org/10.1007/BF02924678

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