Conditional Distribution Approximations

  • John E. Kolassa
Part of the Lecture Notes in Statistics book series (LNS, volume 88)


Often inference for a subset of model parameters is desired, and the others are treated as nuisance parameters. Among the many methods for attacking this problem is conditional inference, in which sufficient statistics for nuisance parameters are conditioned on. Calculations involving these conditional distributions are often quite difficult. This chapter will develop methods for approximating densities and distribution functions for conditional distributions.


Conditional Distribution Nuisance Parameter Saddlepoint Approximation Conditional Inference Cumulant Generate Function 
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Copyright information

© Springer Science+Business Media New York 1997

Authors and Affiliations

  • John E. Kolassa
    • 1
  1. 1.Department of Biostatistics, School of Medicine and DentistryUniversity of RochesterRochesterUSA

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