PROJECT TITLE :
A Hierarchical Bayesian Degradation Model for Heterogeneous Data
Degradation knowledge might be collected from a population with heterogeneous subpopulations. This paper contributes to the development of a brand new statistical modeling and computation method for analyzing heterogeneous degradation information. We tend to adopt the random-coefficient degradation path approach, and propose a hierarchical Bayesian degradation model. To account for the heterogeneity, we have a tendency to model the unit-to-unit variability via random parameters during a Gaussian mixture model. We have a tendency to developed a computationally convenient algorithm that combines Gibbs sampling for parameter estimation furthermore failure-time distribution prediction and Akaike information criterion for determining the number of subpopulations. A numerical example is employed to illustrate the benefits of the proposed methodology over existing strategies that do not explicitly take into account heterogeneity in the degradation knowledge.
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