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Estimation of Parameters of Finite Mixture of Rayleigh Distribution by the Expectation‐Maximization Algorithm

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  • Noor Mohammed
  • Fadhaa Ali

Abstract

In the lifetime process in some systems, most data cannot belong to one single population. In fact, it can represent several subpopulations. In such a case, the known distribution cannot be used to model data. Instead, a mixture of distribution is used to modulate the data and classify them into several subgroups. The mixture of Rayleigh distribution is best to be used with the lifetime process. This paper aims to infer model parameters by the expectation‐maximization (EM) algorithm through the maximum likelihood function. The technique is applied to simulated data by following several scenarios. The accuracy of estimation has been examined by the average mean square error (AMSE) and the average classification success rate (ACSR). The results showed that the method performed well in all simulation scenarios with respect to different sample sizes.

Suggested Citation

  • Noor Mohammed & Fadhaa Ali, 2022. "Estimation of Parameters of Finite Mixture of Rayleigh Distribution by the Expectation‐Maximization Algorithm," Journal of Mathematics, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:jjmath:v:2022:y:2022:i:1:n:7596449
    DOI: 10.1155/2022/7596449
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    References listed on IDEAS

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    1. Gadde Srinivasa Rao & Sauda Mbwambo, 2019. "Exponentiated Inverse Rayleigh Distribution and an Application to Coating Weights of Iron Sheets Data," Journal of Probability and Statistics, Hindawi, vol. 2019, pages 1-13, April.
    2. Stanley Sclove, 1987. "Application of model-selection criteria to some problems in multivariate analysis," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 333-343, September.
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    Cited by:

    1. Fadhaa Ali, 2023. "Bayesian Methods for Estimation the Parameters of Finite Mixture of Inverse Rayleigh Distribution," Mathematical Problems in Engineering, John Wiley & Sons, vol. 2023(1).

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