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Cai, Jing; Shi, Yimin; Liu, Bin

Inference for a series system with dependent masked data under progressive interval censoring. (English) Zbl 1516.62174

J. Appl. Stat. 44, No. 1, 3-15 (2017).
Summary: 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.

Cited in 2 Documents

MSC:

62-XX Statistics

Keywords:

dependent masked data; masking probability; progressively type-I interval censoring; expectation maximization algorithm; Gibbs sampling; Monte Carlo method
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References:

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