An Elegant Method for Generating Multivariate Poisson Random Variable

TL;DR

This paper introduces a novel, efficient method for generating multivariate Poisson data by transforming correlated multivariate Normal data, enabling flexible correlation structures and rate parameters.

Contribution

It presents a new approach that overcomes computational and structural limitations of existing methods for multivariate Poisson data simulation.

Findings

Supports positive and negative correlations

Allows different Poisson rates

Reduces computational complexity

Abstract

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.