A finite mixture of multiple discrete distributions for modelling heaped count data
A new modelling approach, based on finite mixtures of multiple discrete distributions of different multiplicities, is proposed to fit data with a lot of periodic spikes in certain values. An EM algorithm is provided in order to ensure the models' ease-of-fit and then a simulation study is presented...
| Authors: | ; ; |
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| Format: | Article |
| Status: | Accepted version |
| Publication date: | 2017 |
| Country: | España |
| Institution: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repository: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:2445/108966 |
| Online Access: | https://hdl.handle.net/2445/108966 |
| Access Level: | Open access |
| Keyword: | Anàlisi de regressió Assegurances d'accidents Variables (Matemàtica) Regression analysis Accident insurance Variables (Mathematics) |
| Summary: | A new modelling approach, based on finite mixtures of multiple discrete distributions of different multiplicities, is proposed to fit data with a lot of periodic spikes in certain values. An EM algorithm is provided in order to ensure the models' ease-of-fit and then a simulation study is presented to show its efficiency. A numerical application with a real data set involving the length, measured in days, of inability to work after an accident occurs is treated. The main finding is that the model provides a very good fit when working week, calendar week and month multiplicities are taken into account. |
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