Nonlinear Regression With Variance Components
针对均值非线性且含随机效应的模型，提出了估计广义最小二乘和极大似然两种估计方法，并证明了它们的一致性和渐近正态性。
Abstract The nonlinear model with variance components, which combines a nonlinear model for the mean with additive random effects, is applicable to split-plot and nested experiments. We propose two methods of estimation for the parameters of the nonlinear model for the mean: (1) estimated generalized least squares (EGLS), and (2) maximum likelihood (MLE) by the method of scoring. Using a generalization of Klimko and Nelson's theorem on strong consistency of least squares estimators, it is possible to show that both the MLE and the EGLS estimators are strongly consistent, asymptotically normal, and asymptotically efficient.