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Bayesian Analysis of Inverted Kumaraswamy Mixture Model with Application to Burning Velocity of Chemicals

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Listed:
  • Farzana Noor
  • Saadia Masood
  • Mehwish Zaman
  • Maryam Siddiqa
  • Raja Asif Wagan
  • Imran Ullah Khan
  • Ahthasham Sajid

Abstract

Burning velocity of different chemicals is estimated using a model from mixed population considering inverted Kumaraswamy (IKum) distribution for component parts. Two estimation techniques maximum likelihood estimation (MLE) and Bayesian analysis are applied for estimation purposes. BEs of a mixture model are obtained using gamma, inverse beta prior, and uniform prior distribution with two loss functions. Hyperparameters are determined through the empirical Bayesian method. An extensive simulation study is also a part of the study which is used to foresee the characteristics of the presented model. Application of the IKum mixture model is presented through a real dataset. We observed from the results that Linex loss performed better than squared error loss as it resulted in lower risks. And similarly gamma prior is preferred over other priors.

Suggested Citation

  • Farzana Noor & Saadia Masood & Mehwish Zaman & Maryam Siddiqa & Raja Asif Wagan & Imran Ullah Khan & Ahthasham Sajid, 2021. "Bayesian Analysis of Inverted Kumaraswamy Mixture Model with Application to Burning Velocity of Chemicals," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-18, May.
    • Handle: RePEc:hin:jnlmpe:5569652
    DOI: 10.1155/2021/5569652
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