Permutation Framework Sample Clauses

Permutation Framework. ‌ The exact sampling distribution of the chosen test statistic (either the LRT stat- istic or ERV estimator) under the null hypothesis H0 : ERV(12) = 0 is unknown. The permutation test provides a simple way to estimate the exact null distribu- tion of these test statistics. When the null hypothesis is true, the between-subject cross-phenotype covariance matrices for MZ and DZ twins are equivalent, and thus exchangeable. By randomly shuffling the labels of MZ and DZ for those between- subject cross-phenotype covariance matrices in the multivariate GLM model (4.1), we generate a new variance-covariance matrix V within the permuted GLM model that should look like the original one, assuming the null hypothesis is true. The final step of our permutation scheme is to construct the empirical null distribution and the ranking of the original value among all shuffled values of the test statistic gives the permutation-based p-value that can be used to interpret the test statistic and quantify the significance of the test.
Sample 1
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