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...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/397827 |
| Acceso en línea: | https://hdl.handle.net/2117/397827 https://dx.doi.org/10.2436/20.8080.02.117 |
| Access Level: | acceso abierto |
| Palabra clave: | Mathematical statistics Regression analysis Poisson count model Gamma count model Weibull count model Conway-Maxwell-Poisson count model Faddy count model 62J Inferència lineal, regressió 62M Inferència dels processos estocàstics 62P Aplicacions Classificació AMS::62 Statistics::62J Linear inference, regression Classificació AMS::62 Statistics::62M Inference from stochastic processes Classificació AMS::62 Statistics::62P Applications Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica |
| Sumario: | 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. |
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