Testing the ratio of two Poisson means based on an inferential model

The ratio of two Poisson means is commonly used in biological, epidemiological, and medical. In this article, we consider the problem of testing the ratio of two Poisson means and propose a valid and efficient test based on the inference model (IM). The simulation studies show that the type I error and power of the IM-based test are competitive or even better than the six recommended tests: The likelihood ratio, mid-p, logarithmic transformation, and three tests based on the method of variance estimates recovery (MOVER). For R 1, the Rao-MOVER and fiducial-MOVER tests are slightly conservative. The likelihood ratio and logarithmic transformation tests cannot control the type I error well. Therefore, the IM test could be recommended for practical applications. A real numerical example is presented to illustrate the flexibility of the proposed test.

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Publication Details

Journal
Communication in Statistics- Theory and Methods
Published
2024-09-02
DOI
https://doi.org/10.1080/03610926.2024.2395882
Primary Topic
Multi-Criteria Decision Making
Type
article
Field-Weighted Citation Impact
0.00

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article

Testing the ratio of two Poisson means based on an inferential model

Kai Wan, Chao Chen, Xionghui Ou, Yanting Chen et al.
Communication in Statistics- Theory and Methods
Multi-Criteria Decision Making
article

Testing the ratio of two Poisson means based on an inferential model

Kai Wan, Chao Chen, Xionghui Ou, Yanting Chen, Chunxin Wu, Shaofang Kong
article en

Abstract

The ratio of two Poisson means is commonly used in biological, epidemiological, and medical. In this article, we consider the problem of testing the ratio of two Poisson means and propose a valid and efficient test based on the inference model (IM). The simulation studies show that the type I error and power of the IM-based test are competitive or even better than the six recommended tests: The likelihood ratio, mid-p, logarithmic transformation, and three tests based on the method of variance estimates recovery (MOVER). For R 1, the Rao-MOVER and fiducial-MOVER tests are slightly conservative. The likelihood ratio and logarithmic transformation tests cannot control the type I error well. Therefore, the IM test could be recommended for practical applications. A real numerical example is presented to illustrate the flexibility of the proposed test.

Communication in Statistics- Theory and MethodsVol. 54(12)
Wenzhou University (CN), Guangdong Medical College (CN), Guangzhou Medical University (CN)
Basic and Applied Basic Research Foundation of Guangdong Province
Openalex Percentile: Top 82%
Multi-Criteria Decision Making
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