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Extension of the SEM algorithm to a nonlinear hierarchical random intercept model with censored response

Abir El Haj, Yousri Slaoui


Nonlinear mixed-effects models involve both fixed effects and random effects. It differs from the linear mixed-effects models in the nonlinear function of cluster-specific parameter vector and the predictor vector. Some mechanism involve the presence of censored response in the model. In this paper, we propose an extension of the stochastic approximation SEM algorithm to a nonlinear hierarchical random intercept model with censored data. We set some application and we compared our approach to the existing methods.


Gibbs sampling, Hierarchical random intercept models, Non-Linear mixed effects models, SEM algorithm, Censored data.

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