Summary
Generating random samples from multivariate distributions is a common, requirement in many fields of study. Often the complete joint distribution is not specified to the scientist. This paper addresses the situation where only the marginals and the correlation matrix are specified. We suggest a deterministic algorithm, PERMCORR, to approximately achieve the required correlation structure that can be used to get good initial values to standard stochastic algorithms. In many situations the output of PERMCORR is already accurate enough to preempt any need for running an expensive stochastic algorithm. We provide some theoretical justification for our method as well as simulation studies. We also provide a bootstrap technique based on PERMCORR.
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Chakraborty, A. Generating multivariate correlated samples. Computational Statistics 21, 103–119 (2006). https://doi.org/10.1007/s00180-006-0254-y
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DOI: https://doi.org/10.1007/s00180-006-0254-y