Sharif University of Technology Scientia Iranica 1026-3098 25 1 2018 02 01 A proposal for modeling and simulating correlated discrete Weibull variables 386 397 4412 10.24200/sci.2017.4412 EN A. Barbiero Department of Economics, Management and Quantitative Methods, 4 Universit`a degli Studi di Milano, via Conservatorio 7, 20122 Milan, Italy Journal Article 2015 12 20 Researchers in applied sciences are often concerned with multivariate random vari9<br />ables. In particular, multivariate discrete data often arise in many fields (statistical<br />10 quality control, biostatistics, failure and reliability analysis, etc.) and modeling such<br />11 data is a relevant task, as well as simulating correlated discrete data satisfying some spe12<br />cific constraints. Here we consider the discrete Weibull distribution as an alternative to<br />13 the popular Poisson random variable and propose a procedure for simulating correlated<br />14 discrete Weibull random variables, with marginal distributions and correlation matrix as15<br />signed by the user. The procedure indeed relies upon the Gaussian copula model and an<br />16 iterative algorithm for recovering the proper correlation matrix for the copula ensuring<br />17 the desired correlation matrix on the discrete margins. A simulation study is presented,<br />18 which empirically assesses the performance of the procedure in terms of accuracy and<br />19 computational burden, also in relation to the necessary (but temporary) truncation of<br />20 the support of the discrete Weibull random variable. Inferential issues for the proposed<br />21 model are also discussed and are eventually applied to a dataset taken from the literature,<br />22 which shows that the proposed multivariate model can satisfactorily fit real-life correlated<br />23 counts even better than the most popular or recent existing ones. https://scientiairanica.sharif.edu/article_4412_d1dadf84d563de50f658d597d2900c4c.pdf