A finite mixture of multiple discrete distributions for modelling heaped count data

Lluís Bermúdez, Dimitris Karlis and Miguel Santolino

Computational Statistics & Data Analysis, 2017, vol. 112, issue C, 14-23

Abstract: 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.

Keywords: Digit preference; EM algorithm; Multiple Poisson; Work disability days (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:112:y:2017:i:c:p:14-23

DOI: 10.1016/j.csda.2017.02.013

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