• Proceedings of the Korean Statistical Society Conference
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• 2005.11a
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• pp.147-152
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• 2005

본 논문에서는 k개의 포아송 확률변수가 서로 종속 되어 있는 다변량 포아송 분포를 따를 때, 주어진 분산-공분산 행렬 구조를 유지하는 다변량 포아송 확률난수 생성방법에 대해 다루었다. 특히, 확률난수를 생성하기 위해 선형방정식을 푸는 두 가지 수치해석 알고리즘을 제안하였으며, Park 등 (1996)의 다변량 베르누이 확률난수 생성에 활용된 알고리즘과의 연관성을 다루었다.

• Journal of the Korean Data and Information Science Society
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• v.9 no.1
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• pp.47-56
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• 1998

Zero-Inflated Poisson models are mixed models of the Poisson and Bernoulli models. Recently Zero-Inflated Poisson distributions have been used frequently rather than previous Poisson distributions because the developement of industrial technology make few defects in manufacturing process. It is important that univariate Zero-Inflated Poisson distributions are extended to bivariate distributions to generalize the multivariate distributions. In this paper we proposed three types of the bivariate Zero-Inflated Poisson distributions and obtained these moments. We compared the three types of distributions by using the moments.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.161-171
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• 2006

In this paper, we consider the random number generation method for multivariate Poisson distribution with specific covariance matrix. Random number generating method for the multivariate Poisson distribution is considered into two part, by first solving the linear equation to determine the univariate Poisson parameter, then convoluting independent univariate Poisson variates with appropriate expectations. We propose a numerical algorithm to solve the linear equation given the specific covariance matrix.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.177-186
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• 2003

The Zero-Inflated Poisson regression is a model for count data with exess zeros. When the correlated response variables are intrested, we have to extend the univariate zero-inflated regression model to multivariate model. In this paper, we study and simulate the multivariate zero-inflated regression model. A real example was applied to this model. Regression parameters are estimated by using MLE's. We also compare the fitness of multivariate zero-inflated Poisson regression model with the decision tree model.