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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. Torelli, Nicola & Trivellato, Ugo, 1993. "Modelling inaccuracies in job-search duration data," Journal of Econometrics, Elsevier, vol. 59(1-2), pages 187-211, September.
    3. 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.
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