论文标题
自适应学习的线性回归中的两步估计
Two-step estimation in linear regressions with adaptive learning
论文作者
论文摘要
当从辅助模型的非线性最小二乘在第一步中估算了至关重要的，所谓的“增益”参数时，从适应性学习的线性回归中，普通最小二乘估计量的弱一致性和渐近正态性将得出。两步估计器的单数限制分布是正常的，通常会受到第一步的采样不确定性的影响。但是，对于某些参数组合，这个“生成的回归器”问题消失了。
Weak consistency and asymptotic normality of the ordinary least-squares estimator in a linear regression with adaptive learning is derived when the crucial, so-called, `gain' parameter is estimated in a first step by nonlinear least squares from an auxiliary model. The singular limiting distribution of the two-step estimator is normal and in general affected by the sampling uncertainty from the first step. However, this `generated-regressor' issue disappears for certain parameter combinations.
