fwer.ztest: Copula-based multiple z-test which controlls the FWER
In MHTcop: Tests Controlling the FDR / FWER under Certain Copula Models

Description Usage Arguments Details Value References

Perform a multiple (two-sided) z-test controlling the family-wise error rate (FWER) using the procedure described in Stange, Bodnar, Dickhaus (2015).

1	fwer.ztest(sample, mu, sigma = NULL, sigLevel = 0.05)

`sample`	The observed sample
`mu`	The mean μ^*
`sigma`	The estimated covariance matrix (the copula parameter). If it is omitted it will be estimated from an AR(1) model
`sigLevel`	The desired significance level

Let X_1,\cdots,X_n denote an i.i.d. sample with values in {\rm I\!R}^m. Furthermore let μ_j={\rm I\!E}[X_{1,j}] be the component-wise expectations. Then the multiple (two-sided) z-test simultaneously tests the hypotheses H_{0,j}: μ_j = μ_j^* versus the corresponding alternatives H_{1,j}: μ_j\not=μ_j^*.

For usage examples and figure reproduction see vignette('fwer-ztest',package='MHTcop').

Note: If the parameter sigma is passed it needs to be a consistent estimate of the covariance matrix of X_1.

list l, where

l$statistic contains the values of the test statistics,
l$critvalues are the calibrated critical values,
l$test contains the test decisions,
l$etahat is estimated parameter of the Gumbel copula

J. Stange, T. Bodnar and T. Dickhaus (2015). Uncertainty quantification for the family-wise error rate in multivariate copula models. AStA Advances in Statistical Analysis 99.3 (2015): 281-310.

MHTcop documentation built on May 2, 2019, 7:59 a.m.