The Nonlinear Mixed Effects Model with a Smooth Random Effects Density
提出一种联合估计非线性混合效应模型中固定参数和随机效应密度的方法，假设密度光滑但无其他限制，通过级数展开和数值积分计算似然，并应用于药代动力学数据。
The fixed parameters of the nonlinear mixed effects model and the density of the random effects are estimated jointly by maximum likelihood. The density of the random effects is assumed to be smooth but is otherwise unrestricted. The method uses a series expansion that follows from the smoothness assumption to represent the density and quadrature to compute the likelihood. Standard algorithms are used for optimization. Empirical Bayes estimates of random coefficients are obtained by computing posterior modes. The method is applied to data from pharmacokinetics, and properties of the method are investigated by application to simulated data.