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We consider the nonparametric regression model with random design.
Therefore, we introduce a Bayesian hierarchical model with random effects for the system parameters.
In general, the degradation data are modeled by a nonlinear regression model with random coefficients.
Estimation of the population mean under the regression model with random components is considered.
In this paper we discuss optimal designs for a Poisson regression model with random intercept.
These rare events depend on the output of a physical model with random input variables.
Two-level regression model with random intercepts was used to investigate the contribution of district's characteristics to spatial variation in amenable mortality (MIXED procedure in SPSS).
To account for potential correlations among these segments, a hierarchical Poisson log-normal model with random effects was developed.
A bi-exponential model with random coefficients is introduced to represent the nonlinear deterioration path of the MEAs.
We first discuss how to turn a constrained model with random rough variables into crisp equivalent models.
It is shown that some of these designs are also optimal under the model with random neighbor effects.
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