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Adding a Likelihood Q-Ratio Test #83
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Hi @adam2392 , I'm happy to receive a PR that implements this additional test in the results dataframe. You will need to do a decent job of explaining the test in the documentation as it is obscure. Do you have any examples of this test being used in biomedical research papers? Joses |
Hi @josesho so in terms of the documentation, would this just be adding documentation into:
Or are there additional files to change regarding this.
No because it is a new publication, but the test is proven to be more robust (compared to the t-test) in terms of power for even small changes away from a perfectly Gaussian model, and it is better then the Wilcoxon rank-sum test in this aspect. So in terms of biomedical data, this would be nice because typically no data is perfectly modeled as a single Gaussian. I am using it though for my own research now as a result. The paper and pip package came out of my university, so I found out about it as soon as it was published. |
@adam2392 also, just as a clarification LqRT does not assume a gross-error model, it assumes normal distribution, but degrades less if there is contamination in the sample. lastly, there is a preprint that discusses this package specifically, as opposed to the general LqRT; it can be found here. |
Closed with #89 |
Hi, I was wondering what your thoughts on adding a robust statistic, such as the LqrT either to replace the t-test, or to add an additional column in the statistical testing? A quick summary: Compared to Wilcoxon Rank-Sum tests, it is more robust when the model is misspecified under a gross error model. See figure 9 of the paper for a most compelling result.
Proposed solution:
Adding the https://github.com/alyakin314/lqrt package into the requirements.txt and incorporating that into the statistical results dataframe result.
Reference:
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