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

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  • Bermúdez, Lluís
  • Karlis, Dimitris
  • Santolino, Miguel

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.

Suggested Citation

  • Bermúdez, Lluís & Karlis, Dimitris & Santolino, Miguel, 2017. "A finite mixture of multiple discrete distributions for modelling heaped count data," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 14-23.
    • Handle: RePEc:eee:csdana:v:112:y:2017:i:c:p:14-23
    DOI: 10.1016/j.csda.2017.02.013
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    References listed on IDEAS

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    1. Wang, B. & Wertelecki, W., 2013. "Density estimation for data with rounding errors," Computational Statistics & Data Analysis, Elsevier, vol. 65(C), pages 4-12.
    2. Jean Philippe Boucher & Miguel Santolino, 2010. "Discrete distributions when modeling the disability severity score of motor victims," IREA Working Papers 201005, University of Barcelona, Research Institute of Applied Economics, revised Feb 2010.
    3. Torelli, Nicola & Trivellato, Ugo, 1993. "Modelling inaccuracies in job-search duration data," Journal of Econometrics, Elsevier, vol. 59(1-2), pages 187-211, September.
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