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UNLABELLED Permutation test is a popular technique for testing a hypothesis of no effect, when the distribution of the test statistic is unknown. To test the equality of two means, a permutation test might use a test statistic which is the difference of the two sample means in the univariate case. In the multivariate case, it might use a test statistic(More)
Characterizing the extent of linkage disequilibrium (LD) in the genome is a pre-requisite for association mapping studies. Patterns of LD also contain information about the past demography of populations. In this study, we focus on the Icelandic population where LD was investigated in 12 regions of approximately 15 cM using regularly spaced microsatellite(More)
This article discusses specific assumptions necessary for permutation multiple tests to control the Familywise Error Rate (FWER). At issue is that, in comparing parameters of the marginal distributions of two sets of multivariate observations, validity of permutation testing is affected by all the parameters in the joint distributions of the observations.(More)
Permutation test is a popular technique for testing a hypothesis of no effect, when the distribution of the test statistic is unknown. To test the equality of two means, a permutation test might use a test statistic which is the difference of the two sample means in the univariate case. In the multivariate case, it might use a test statistic which is the(More)
In current practice, such as GWAS (genome-wide association studies), permutation is often applied to multiple testing for association between large number of features [e.g. single nucleotide polymorphisms (SNPs)] and phenotypes (Hahn et al., 2008). Inferring that there is a difference between the phenotypic groups X and Y in some of the features is not very(More)
We model two time and space scales discrete observations by using a unique continuous diffusion process with time dependent coefficient. We define new parameters for the large scale model as functions of the small scale distribution cumulants. We use the non-uniform distribution of the observation time intervals to obtain consistent and unbiased estimators(More)