A Latent Variable Approach to the Analysis of Progressively Hybrid Censored Masked Data

Sanjeev K Tomer (), M S Panwar () and Himanshu Rai ()
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Sanjeev K Tomer: Banaras Hindu University
M S Panwar: Banaras Hindu University
Himanshu Rai: Tata Institute of Social Sciences

Methodology and Computing in Applied Probability, 2025, vol. 27, issue 4, 1-28

Abstract: Abstract In this article, we are dealing with two important issues that arise in the competing risks analysis of series system lifetime data. First, we deal with incomplete lifetimes of the system’s components, observed under a Type-I progressive hybrid censoring scheme. Second, we examine situations where the exact cause of failure of any system is unknown. To address these issues, we develop models incorporating cause dependent and time dependent masking probabilities. The Maxwell distribution is considered as the lifetime model for components, and parameter estimation is performed using maximum likelihood and Bayesian approaches. In a simulation study, the derived methodology is explored for varying sample sizes and different censoring patterns under cause and time dependent masking mechanisms. For real-life illustration, the data set of 10,000 hard drives is analyzed. To choose a better model in the presence of various masking options, the predictive power and deviance information criterion are also explored.

Keywords: Bayesian inference; Competing risks; E-M algorithm; Masking probability; Predictive power; 62N02; 62F10; 62F15; 60E05 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11009-025-10197-z

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