Abstract
The analysis of masked cause of failure data is an important area in the reliability analysis. Prior researches mostly included masking probability as a part of likelihood function to handle masked competing risks analysis which were much time-consuming, high costly and complicated computationally. To optimize time and cost and also overcome complexity of calculation, in this paper, a new two-step approach is presented which is based on multiple imputation of masked causes of failure via some machine learning algorithms. Then, in the second step, the filled-in competing risks data are analysed using standard maximum likelihood approach. For competing risks data, the superiority of the proposed method comparing with the prior ones is evaluated in ML Estimations (MLE) of Life-time parameters via several simulation studies and applying on real data. Also, sensitivity analysis for biasness versus different sample sizes is exemplified.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
