2026-04-13T02:52:30Zhttps://recercat.cat/oai/request

oai:recercat.cat:2445/1089662025-12-05T05:56:14Zcom_2072_1057col_2072_478917col_2072_478919

00925njm 22002777a 4500 dc Bermúdez, Lluís author Karlis, Dimitris author Santolino, Miguel author 2017-03-27T12:39:52Z 2019-02-24T06:10:14Z 2017-02-24 2017-03-27T12:39:53Z 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. Anàlisi de regressió Assegurances d'accidents Variables (Matemàtica) Regression analysis Accident insurance Variables (Mathematics) A finite mixture of multiple discrete distributions for modelling heaped count data