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• W2997167814 abstract "This study proposes a random effects model based on inverse Gaussian process, where the mixture normal distribution is used to account for both unit-specific and subpopulation-specific heterogeneities. The proposed model can capture heterogeneities due to subpopulations in the same population or the units from different batches. A new Expectation-Maximization (EM) algorithm is developed for point estimation and the bias-corrected bootstrap is used for interval estimation. We show that the EM algorithm updates the parameters based on the gradient of the loglikelihood function via a projection matrix. In addition, the convergence rate depends on the condition number that can be obtained by the projection matrix and the Hessian matrix of the loglikelihood function. A simulation study is conducted to assess the proposed model and the inference methods, and two real degradation datasets are analyzed for illustration." @default.
• W2997167814 created "2020-01-10" @default.
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• W2997167814 date "2020-05-01" @default.
• W2997167814 modified "2023-10-17" @default.
• W2997167814 title "Degradation modeling with subpopulation heterogeneities based on the inverse Gaussian process" @default.
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• W2997167814 doi "https://doi.org/10.1016/j.apm.2019.12.017" @default.
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