Abstract Generating multivariate Poisson random variables is essential in many applications, such as multi echelon supply chain systems, multi-item/multi-period pricing models, accident monitoring systems, etc. Current simulation methods suffer from limitations ranging from computational complexity to restrictions on the structure of the correlation matrix, and therefore are rarely used in management science.
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Summary. 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.
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Communications in Statistics - Simulation and Computation
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Methods and Case Studies
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Journal of Statistical Software
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We consider a Backward Simulation approach to modelling correlated Poisson processes and compare correlation time structure of that with the Forward Simulation. The paper is a modified version of the book chapter "Correlated Poisson Processes and Their Applications in Financial Modeling", in “Financial Signal Processing and Machine Learning”, Ed. by A. Akansu, S. Kulkarni and D. Malioutov, 2016, John Wiley & Sons, 191-232.
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Generating multivariate random vectors is a crucial part of the input analysis involved in discrete-event stochastic simulation modeling of multivariate systems. The NORmal-To-Anything (NORTA) algorithm, in which generating the correlation matrices of normal random vectors is the most important task, is one of the most efficient methods in this area. In this algorithm, we need to solve the so-called correlation-matching problem in which some complicated equations that are defined to obtain the correlation matrix of normal random variables need to be solved. Many researchers have tried to solve these equations by three general approaches of (1) solving nonlinear equations analytically, (2) solving equations numerically, and (3) solving equations by simulation. This paper suggests the use of artificial neural networks, called Perceptron, to solve the corresponding problem. Using three simulation experiments, the applicability of the proposed methodology is described and the results ob.
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International Journal of Production Research
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Communications in Statistics - Theory and Methods
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