Evaluation of Approximation of Fisher Information Matrix in Poisson Mixture Model using High Performance Computing


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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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• Fisher information matrix(FIM) is an essential part in the computation of maximum likelihood estimation(MLE) as well as in obtaining their standard errors • Computation of FIM is resource intensive in some widely used models such as mixture distributions • Neerchal and Morel(1993), Raim (2014), and Raim et. al.(2014) have provided approximations to the FIM • In this project, we apply this approximation idea to a mixture of two Poisson distributions • A program in C with MPI is designed to test the performance of our approximation under various selections of parameter values