2026-04-17T06:45:54Zhttps://recercat.cat/oai/requestoai:recercat.cat:2445/1089662025-12-05T05:56:14Zcom_2072_1057col_2072_478917col_2072_478919
RECERCAT
author
Bermúdez, Lluís
author
Karlis, Dimitris
author
Santolino, Miguel
2017-03-27T12:39:52Z2019-02-24T06:10:14Z2017-02-242017-03-27T12:39:53Z
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.
cc-by-nc-nd (c) Elsevier B.V., 2017 http://creativecommons.org/licenses/by-nc-nd/3.0/es info:eu-repo/semantics/openAccess
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
info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion