An Elegant Method for Generating Multivariate Poisson Data

نویسندگان

  • Inbal Yahav
  • Galit Shmueli
چکیده

Generating multivariate Poisson data is essential in many applications. Current simulation methods suffer from limitations ranging from computational complexity to restrictions on the structure of the correlation matrix. We propose a computationally efficient and conceptually appealing method for generating multivariate Poisson data. The method is based on simulating multivariate Normal data and converting them to achieve a specific correlation matrix and Poisson rate vector. This allows for generating data that have positive or negative correlations as well as different rates.

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تاریخ انتشار 2008