The nonhomogeneous Poisson process is commonly utilized to model the occurrence of events over time. The identification of nonhomogeneous Poisson process relies on the intensity function, which can be difficult to determine. A straightforward approach is to set the intensity function to a constant value, resulting in a homogeneous Poisson process. However, it is crucial to assess the homogeneity of the intensity function through an appropriate test beforehand. Failure to confirm homogeneity leads to an infinite-dimensional problem that cannot be comprehensively resolved. In this study, we analyzed data on the number of passengers using the Tehran metro. Our homogeneity test showed a nonhomogeneous arrival rate of passengers, prompting us to explore different functions to estimate the intensity function. We considered four functions and used a piecewise function to determine the best intensity function. Our findings showed significant differences between the two models, highlighting the effectiveness of the piecewise function model in predicting the number of metro passengers.
Yarmohammadi, M., Afshar, A., Mahmoudvand, R., & Nasiri, P. (2022). Predicting intensity function of nonhomogeneous Poisson process. Journal of Statistical Modelling: Theory and Applications, 3(2), 39-50. doi: 10.22034/jsmta.2023.19960.1094
MLA
Masoud Yarmohammadi; Ada Afshar; Rahim Mahmoudvand; Parviz Nasiri. "Predicting intensity function of nonhomogeneous Poisson process", Journal of Statistical Modelling: Theory and Applications, 3, 2, 2022, 39-50. doi: 10.22034/jsmta.2023.19960.1094
HARVARD
Yarmohammadi, M., Afshar, A., Mahmoudvand, R., Nasiri, P. (2022). 'Predicting intensity function of nonhomogeneous Poisson process', Journal of Statistical Modelling: Theory and Applications, 3(2), pp. 39-50. doi: 10.22034/jsmta.2023.19960.1094
VANCOUVER
Yarmohammadi, M., Afshar, A., Mahmoudvand, R., Nasiri, P. Predicting intensity function of nonhomogeneous Poisson process. Journal of Statistical Modelling: Theory and Applications, 2022; 3(2): 39-50. doi: 10.22034/jsmta.2023.19960.1094
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