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We applied LASSO regularization rather than significance values, as significance levels have an arbitrary threshold as well as either the disadvantage of multiple testing problems or lower power when applying multiple testing corrections (for further explanation see35).
In multiple testing problems where informative covariates can be naturally assigned to hypotheses, e.g. hypotheses are arranged in spacetime, we propose and validate a likelihood based method that provides more powerful inference while retaining control over false discovery rate.
Note that FAM-MDR* may well be the most powerful approach but, like PGMDR, it does not appropriately handle multiple testing problems.
GWAS are limited because the cost prohibits acquisition of the sample size needed to overcome the multiple testing problems inherent in gene-gene analyses.
However, the possibility of false positives arising from the multiple testing problems should be small since the reported P value is significant enough to survive even the most conservative Bonferroni correction.
Some criticisms involve the specific execution of the method, such as the use of a very small number of inbred strains, or insufficient control of false discoveries due to multiple testing problems [9], [10].
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This has been called the multiple testing problem or the problem of multiplicity [ 8].
Due to the high number of tested hypotheses (number of molecular features, 5,891), the multiple testing problem was addressed.
Through simple mathematics, we convert the dependence detection problem to a multiple testing problem.
Even if we only conduct a formal statistical test that compares the two phenotypes for each tissue, the multiple testing problem is greatly complicated.
In this course you'll learn various statistics topics including multiple testing problem, error rates, error rate controlling procedures, false discovery rates, q-values and exploratory data analysis.
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