Аннотация:
Common X^2 tests are very well known and frequently applied in statistical analyses in particular for discrete models. An application to genetic association studies is considered, where a large number M, say, of 2x3 contingency tables is simultaneously tested. A method controlling the family wise error rate is shown, which makes use of an effective number of tests in Sidak multiplicity correction favor. This method considers an approximation of the full M-dimensional distribution of the involved X^2 test statistics, by a product of k-dimensional marginal distributions. A challenge of this procedure is an efficient computation of the k-dimensional distributions. Besides time consuming Monte Carlo procedures, there are only few implementations for even smaller dimensions of multivariate distributions. Existing formulas for the cumulative distribution function of a multivariate X^2 distribution are now implemented for an approximations with k equal to up to 4.
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