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On the Bayesian inference of Kumaraswamy distributions based on censored samples

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  • Indranil Ghosh
  • Saralees Nadarajah

Abstract

In this article we discuss Bayesian estimation of Kumaraswamy distributions based on three different types of censored samples. We obtain Bayes estimates of the model parameters using two different types of loss functions (LINEX and Quadratic) under each censoring scheme (left censoring, singly type-II censoring, and doubly type-II censoring) using Monte Carlo simulation study with posterior risk plots for each different choices of the model parameters. Also, detailed discussion regarding elicitation of the hyperparameters under the dependent prior setup is discussed. If one of the shape parameters is known then closed form expressions of the Bayes estimates corresponding to posterior risk under both the loss functions are available. To provide the efficacy of the proposed method, a simulation study is conducted and the performance of the estimation is quite interesting. For illustrative purpose, real-life data are considered.

Suggested Citation

  • Indranil Ghosh & Saralees Nadarajah, 2017. "On the Bayesian inference of Kumaraswamy distributions based on censored samples," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(17), pages 8760-8777, September.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:17:p:8760-8777
    DOI: 10.1080/03610926.2016.1193197
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    Cited by:

    1. Nadeem Akhtar & Sajjad Ahmad Khan & Emad A. A. Ismail & Fuad A. Awwad & Akbar Ali Khan & Taza Gul & Haifa Alqahtani, 2024. "Analyzing quantitative performance: Bayesian estimation of 3-component mixture geometric distributions based on Kumaraswamy prior," Statistical Papers, Springer, vol. 65(7), pages 4431-4451, September.

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