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Saddlepoint p-values for a class of location-scale tests
Abd El-Raheem M Abd El-Raheem1, Haidy N Mohamed1, Ehab F Abd-Elfattah1
1Department of Mathematics, Faculty of Education, Ain Shams University, Cairo, Egypt.
Journal of Biopharmaceutical Statistics
|June 10, 2024
Summary
This study introduces a saddlepoint approximation method to accurately estimate p-values for non-parametric location-scale tests. The new approach is faster and more precise than traditional normal approximation methods.
Area of Science:
- Statistics
- Non-parametric statistics
Background:
- Accurate p-value approximation is crucial for hypothesis testing.
- Traditional methods like normal approximation may lack precision for non-parametric tests.
Purpose of the Study:
- To approximate the exact p-value for a class of non-parametric, two-sample location-scale tests.
- To introduce and validate the saddlepoint approximation method for this purpose.
Main Methods:
- Formulating non-parametric two-sample location-scale tests as linear rank tests.
- Deriving the permutation distribution from a random allocation design.
- Applying the saddlepoint approximation method to calculate p-values.
Main Results:
- The saddlepoint approximation method demonstrates high accuracy in approximating exact p-values.
- The proposed method is computationally efficient, requiring minimal time and calculations.
- Comparison with normal approximation shows superior accuracy of the saddlepoint method.
Conclusions:
- The saddlepoint approximation offers an accurate and efficient alternative for p-value calculation in non-parametric location-scale tests.
- The method's effectiveness is validated through real data applications and simulation studies.
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