Unusual-event processes for count data
At least one unusual event appears in some count datasets. It will lead to a more concentrated (or dispersed) distribution than the Poisson, gamma, Weibull, Conway-Maxwell-Poisson (CMP), and Faddy (1997) models can accommodate. These well-known count models are based on the monotonic rates of intera...
| Authors: | , |
|---|---|
| Format: | article |
| Publication Date: | 2022 |
| Country: | España |
| Institution: | Universitat Autònoma de Barcelona |
| Repository: | Dipòsit Digital de Documents de la UAB |
| Language: | English |
| OAI Identifier: | oai:ddd.uab.cat:264543 |
| Online Access: | https://ddd.uab.cat/record/264543 https://dx.doi.org/urn:doi:10.2436/20.8080.02.117 |
| Access Level: | Open access |
| Keyword: | Poisson count model Gamma count model Weibull count model Conway-Maxwell-Poisson count model Faddy count model |
| Summary: | At least one unusual event appears in some count datasets. It will lead to a more concentrated (or dispersed) distribution than the Poisson, gamma, Weibull, Conway-Maxwell-Poisson (CMP), and Faddy (1997) models can accommodate. These well-known count models are based on the monotonic rates of interarrival times between successive events. Under the assumption of non-monotonic rates and independent exponential interarrival times, a new class of parametric models for unusual-event (UE) count data is proposed. These 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. |
|---|