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On the use of saddlepoint approximations in highdimensional inference

By Jens Ledet Jensen
Thiele Research Reports
No. 04, September 2016
Abstract:

Inference in high dimensional parameter space poses many challenges. One of these is the possible use of saddlepoint approximations. Motivated by a recent use of the saddlepoint approximation to construct a conditional test, we argue that the precision is questionable. We illustrate this by an example giving a 50% relative error in the calculation of the \(p\)-value. A power study of the underlying test reveals a low power in many situations. As an alternative it is suggested to use the likelihood ratio test.

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