ASYMPTOTIC THEORY FOR M-ESTIMATORS OVER A CONVEX KERNEL
研究了凸核上M估计量的分布收敛性，在凸性假设下以最小条件得到极限分布，并应用于多元线性回归模型，适用于不同回归变量和误差序列。
We study the convergence in distribution of M -estimators over a convex kernel. Under convexity, the limit distribution of M -estimators can be obtained under minimal assumptions. We consider the case when the limit is arbitrary, not necessarily normal. If some Taylor expansions hold, the limit distribution is stable. As an application, we examine the limit distribution of M -estimators for the multivariate linear regression model. We obtain the distributional convergence of M -estimators for the multivariate linear regression model for a wide range of sequences of regressors and different types of conditions on the sequence of errors.