Analysis of Masked Competing Risks Data Using Machine Learning Imputation Methods
Résumé
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.
In this paper, a new two-step approach is presented which is based on imputation of masked causes of failure via some machine learning algorithms as the first step.
Then, in the second step, the filled-in competing risks data are analyzed using standard maximum likelihood approach. 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.