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An alternative perspective on the mixture estimation problem

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  • Nagode, M.
  • Fajdiga, M.

Abstract

The paper presents an alternative perspective on the mixture estimation problem. First, observations are counted into a histogram. Secondly, rough and enhanced parameter estimation followed by the separation of observations is done. Finally, the residue is distributed between the components by the Bayes decision rule. The number of components, the mixture component parameters and the component weights are modelled jointly, no initial parameter estimates are required, the approach is numerically stable, the number of components has no influence upon the convergence and the speed of convergence is very high. The alternative perspective is compared to the EM algorithm and verified through several data sets. The presented algorithm showed significant advantages compared to the competitive methods and has already been successfully applied in reliability and fatigue analyses.

Suggested Citation

  • Nagode, M. & Fajdiga, M., 2006. "An alternative perspective on the mixture estimation problem," Reliability Engineering and System Safety, Elsevier, vol. 91(4), pages 388-397.
    • Handle: RePEc:eee:reensy:v:91:y:2006:i:4:p:388-397
    DOI: 10.1016/j.ress.2005.02.005
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    References listed on IDEAS

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    1. J. G. Booth & J. P. Hobert, 1999. "Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 265-285.
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    Cited by:

    1. Guikema, S.D. & Quiring, S.M., 2012. "Hybrid data mining-regression for infrastructure risk assessment based on zero-inflated data," Reliability Engineering and System Safety, Elsevier, vol. 99(C), pages 178-182.

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