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L2E estimation of mixture complexity for count data

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  • Umashanger, T.
  • Sriram, T.N.

Abstract

For count data, robust estimation of the number of mixture components in finite mixtures is revisited using L2 distance. An information criterion based on L2 distance is shown to yield an estimator, which is also shown to be strongly consistent. Monte Carlo simulations show that our estimator is competitive with other procedures in correctly determining the number of components when the data comes from Poisson mixtures. When the data comes from a negative binomial mixture but the postulated model is a Poisson mixture, simulations show that our estimator is highly competitive with the minimum Hellinger distance (MHD) estimator in terms of robustness against model misspecification. Furthermore, we illustrate the performance of our estimator for a real dataset with overdispersion and zero-inflation. Computational simplicity combined with robustness property makes the L2E approach an attractive alternative to other procedures in the literature.

Suggested Citation

  • Umashanger, T. & Sriram, T.N., 2009. "L2E estimation of mixture complexity for count data," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4243-4254, October.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:12:p:4243-4254
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    References listed on IDEAS

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    Cited by:

    1. Chee, Chew-Seng, 2017. "A mixture model-based nonparametric approach to estimating a count distribution," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 34-44.
    2. Jingjing Wu & Rohana J. Karunamuni, 2018. "Efficient and robust tests for semiparametric models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(4), pages 761-788, August.
    3. Nalan Basturk & Lennart Hoogerheide & Herman K. van Dijk, 2021. "Bayes estimates of multimodal density features using DNA and Economic Data," Tinbergen Institute Discussion Papers 21-017/III, Tinbergen Institute.

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