Minimum Hellinger Distance Estimation for Multivariate Location and Covariance
通过最小化非参数密度估计与模型族之间的Hellinger距离，得到多元数据中位置和协方差的估计量，该估计量具有仿射不变性、一致性和渐近正态性，且稳健性优于传统M估计。
Abstract The Hellinger distance between a nonparametric density estimator and a model family is minimized to produce estimates of location and covariance in multivariate data. With suitable restrictions on the density estimators and the model family, these minimum Hellinger distance estimators (MHDE's) are shown to be affine invariant, consistent, and asymptotically normal. The robustness of the MHDE as measured by the breakdown point compares favorably against the previously studied M-estimators. Monte Carlo results suggest that the MHDE's are an attractive robust alternative to the usual sample means and covariance matrix.