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Bayesian inference from the mixture of half-normal distributions under censoring

Authors:

Tabassum Naz Sindhu ,

Quaid-i-Azam University, Pakistan, PK
About Tabassum
Department of Statistics
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Hafiz MR Khan,

Texas Tech University Health Sciences Center, USA, US
About Hafiz MR
Department of Public Health
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Zawar Hussain,

Quaid-i-Azam University, Pakistan, PK
About Zawar
Department of Statistics
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Bander Al-Zahrani

King Abdulaziz University, Saudi Arabia, SA
About Bander
Departament of Statistics
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Abstract

This study considers the Bayesian inference for the mixture of two components of half-normal distribution using non-informative and informative prior. Several of its structural properties were derived, including explicit expression for mean, median, mode, reliability and hazard rate functions. Due to cost and time constraints, in most lifetime testing experiments censoring is an obligatory feature of lifetime datasets. We investigated Bayesian estimation of the parameters using various loss functions. The prior belief of the mixture model is represented by the uniform and square-root inverted gamma priors. Some properties of the model with graphs of the mixture density and hazard function are also discussed. The efficiencies of the proposed set of estimates of the mixture model parameters were studied through simulation and a real life dataset. Posterior risks of the Bayes estimators are evaluated and compared to explore the effect of prior belief and loss functions.

How to Cite: Sindhu, T.N., Khan, H.M., Hussain, Z. and Al-Zahrani, B., 2018. Bayesian inference from the mixture of half-normal distributions under censoring. Journal of the National Science Foundation of Sri Lanka, 46(4), pp.587–600. DOI: http://doi.org/10.4038/jnsfsr.v46i4.8633
Published on 31 Dec 2018.
Peer Reviewed

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