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Communication Dans Un Congrès Année : 2020

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.
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Dates et versions

hal-03654432 , version 1 (28-04-2022)

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  • HAL Id : hal-03654432 , version 1

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Hasan Misaii, Samaneh Eftekhari Mahabadi, Negin Jafari, Haghighi Firoozeh. Analysis of Masked Competing Risks Data Using Machine Learning Imputation Methods. 6th Seminar on Reliability and its Applications, Aug 2020, Tehran, Iran. ⟨hal-03654432⟩
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