Uniformly adaptive estimation for models with arma errors
提出一种两步半参数估计量，用于非线性回归模型（误差为自回归移动平均过程），该估计量具有均匀自适应性，即其期望损失收敛于基于真实密度的最大似然估计的渐近期望损失，且收敛性不依赖于参数值或未知密度形式。
A semiparametric estimator based on an unknown density isuniformly adaptive if the expected loss of the estimator converges to the asymptotic expected loss of the maximum liklihood estimator based on teh true density (MLE), and if convergence does not depend on either the parameter values or the form of the unknown density. Without uniform adaptivity, the asymptotic expected loss of the MLE need not approximate the expected loss of a semiparametric estimator for any finite sample I show that a two step semiparametric estimator is uniformly adaptive for the parameters of nonlinear regression models with autoregressive moving average errors.