Abstract:

The problem of sampling multivariate count variables has practical significance. Ref.[1]proposed an algorithm for sampling multivariate count random variables based on C-vine copulas, by which the parameters 

ρi,j|D

 of edge 

ei,j|D

 of the C-vine structure are estimated by optimizing the difference between the sample partial correlation 

σ︿i,j|D

 and the partial correlation 

σi,j|D

 calculated from the prespecified correlation matrix by the Pearson recurrence formula, where 

D

 is a conditioning node set. We introduce the concept of marginal regular vine copula, which leads to directly optimizing the difference between the sample correlation 

σ︿ij

 and the targeted correlation 

σij

 for pairs of variables. Three simulation studies illustrate that the new sampling method generates more accurate results than the C-vine sampling method in Ref.[1]and the Naive sampling method in Ref.[3]. The sampling algorithm routines are implemented in Python as package countvar in PyPi.

Key words: C-vine copula, marginal regular vine copula, multivariate count random variable, naive sampling method, regular vine, sampling