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Using flexible Bayesian regression models with random effects allowed us to estimate absolute risks without strong modeling assumptions.
In this paper, we construct cost consensus models with random opinions.
Serial FVCpp were plotted using linear mixed effects models with random coefficients for between-subject variability.
We used linear mixedeffects models with random intercepts and slopes to assess the relationship between the HDRS total score and the two mentioned features.
Linear regression models with random effects for correlation within camps were applied with human retinol concentration as outcome.
We use linear multilevel models with random intercept and we account for sample selection.
Many population models with random interference have been investigated [55 60].
Table 1 represents the summary of related literature for inventory models with random planning horizon.
We consider the issue of fitting multivariate nonlinear differential equation models with random effects and unknown initial conditions to irregularly spaced data.
Fitting Nonlinear Ordinary Differential Equation Models with Random Effects and Unknown Initial Conditions Using the Stochastic Approximation Expectation-Maximization (SAEM) Algorithm.
This further confirmed that the models with random data stratification perform better than those with fixed stratification.
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