Statistical Papers

, 31:83 | Cite as

Views on conditional and marginal methods of statistical inference

  • D. A. S. Fraser


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.


Conditional Distribution Marginal Distribution Transformation Model Nuisance Parameter Marginal Likelihood 
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Copyright information

© Springer-Verlag 1990

Authors and Affiliations

  • D. A. S. Fraser
    • 1
  1. 1.Department of MathematicsYork UniversityNorth YorkCanada

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