Saddlepoint Series for Densities

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

Abstract

In many statistical applications, approximations to the probability that a random variable exceeds a certain threshold value are important. Such approximations are useful, for example, in constructing tests and confidence intervals, and for and calculating p-values. Edgeworth series converge uniformly quickly over the entire possible range of the random variable, when error is measured in an absolute sense. Often times, relative error behavior is more important than absolute error behavior; an error of .005 is of little importance when considering tests of approximate size .05 but is of great importance when considering tests of approximate size .001. Saddlepoint methodology is a method for achieving in many cases uniform bounds on relative error over the range of the distribution. This work was pioneered by Daniels (1954).

Keywords

Steep Descent Exponential Family Steep Descent Method Saddlepoint Approximation Inverse Gaussian Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1994

Authors and Affiliations

  • John E. Kolassa
    • 1
  1. 1.Department of BiostatisticsUniversity of Rochester, School of Medicine and DentistryRochesterUSA

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