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Inference for a series system with dependent masked data under progressive interval censoring

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  • Jing Cai
  • Yimin Shi
  • Bin Liu

Abstract

This paper considers the statistical analysis of masked data in a series system with Burr-XII distributed components. Based on progressively Type-I interval censored sample, the maximum likelihood estimators for the parameters are obtained by using the expectation maximization algorithm, and the associated approximate confidence intervals are also derived. In addition, Gibbs sampling procedure using important sampling is applied for obtaining the Bayesian estimates of the parameters, and Monte Carlo method is employed to construct the credible intervals. Finally, a simulation study is proposed to illustrate the efficiency of the methods under different removal schemes and masking probabilities.

Suggested Citation

  • Jing Cai & Yimin Shi & Bin Liu, 2017. "Inference for a series system with dependent masked data under progressive interval censoring," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(1), pages 3-15, January.
    • Handle: RePEc:taf:japsta:v:44:y:2017:i:1:p:3-15
    DOI: 10.1080/02664763.2016.1156658
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    Cited by:

    1. Jing Cai & Yimin Shi & Bin Liu, 2017. "Statistical analysis for masked system life data from Marshall‐Olkin Weibull distribution under progressive hybrid censoring," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(6), pages 490-501, September.

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