Multivariate Zero-Inflated Poisson Regression A PROJECT SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA BY Yang Wang IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE
نویسنده
چکیده
In this report, we develop a procedure to analyze the relationship between the observed multi-dimensional counts and a set of explanatory variables. The counts follow a multivariate Poisson distribution or a multivariate zero-inflated Poisson distribution. Maximum likelihood estimates (MLE) for the model parameters are obtained by the Newton-Raphson (NR) iteration and the expectation-maximization (EM) algorithm, respectively. In Newton-Raphson method, the first and second derivatives of the loglikelihood function are derived to carry out the numerical evaluation. Formulas using EM algorithm are also introduced. A comparison of the estimation performance is made from simulation studies.
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