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Estimating the Parameters of Periodic Bivariate Compound Poisson Process by Inference for Margins Method
Ali Sakhaei , Parviz Nasiri
Abstract:   (1201 Views)

The non-homogeneous bivariate compound Poisson process with short term periodic intensity function is used for modeling the events with seasonal patterns or periodic trends. In this paper, this process is carefully introduced. In order to characterize the dependence structure between jumps, the Levy copula function is provided. For estimating the parameters of the model, the inference for margins method is used. As an application, this model is fitted to an automobile insurance dataset with inference for margins method and its accuracy is compared with the full maximum likelihood method. By using the goodness of fit test, it is confirmed that this model is appropriate for describing the data.

Keywords: Non homogeneous Poisson Process, Levy Copula, Short-term Periodic, Inference for Margins Method, Levy Process.
Full-Text [PDF 6836 kb]   (130 Downloads)    
Type of Study: Applied | Subject: Probability & Stochastic Processes
Received: 2018/01/8 | Accepted: 2018/06/29
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مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences
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