2021/04/30 by Wanrudee Skulpakdee, Skulpakdee, Wanrudee, Mongkol Hunkrajok +1
Decision Sciences · Mathematics · #62P10 #Applications (stat.AP) #FOS: Computer and information sciences #Probability and Risk Models #Statistical Distribution Estimation and Applications #Statistical Methods and Bayesian Inference #msc:62P10 #stat.AP
paper · pdf · doi:10.48550/arxiv.2104.15087
22 pages, 5 figures
openalex publication_date 2021/04/30 · arxiv created 2021/05/06 · arxiv updated 2021/05/07 · openalex created_date 2021/05/10 · openalex updated_date 2026/07/28
At least one unusual event appears in some count datasets. It will lead to a more concentrated (or dispersed) distribution than the Poisson, the gamma, the Weibull, and the Conway-Maxwell-Poisson (CMP) can accommodate. These well-known count models are based on the equal rates of interarrival times between successive events. Under the assumption of unequal rates (one unusual event) and independent exponential interarrival times, a new class of parametric models for single-unusual-event (SUE) count data is proposed. These two models are applied to two empirical applications, the number of births and the number of bids, and yield considerably better results to the above well-known count models.