Minimum Hellinger Distance Estimation for Multivariate Location and Covariance
作者:
RoyN. Tamura,
DennisD. Boos,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1986)
卷期:
Volume 81,
issue 393
页码: 223-229
ISSN:0162-1459
年代: 1986
DOI:10.1080/01621459.1986.10478264
出版商: Taylor & Francis Group
关键词: Kernel estimator;Affine invariance;Break-down point
数据来源: Taylor
摘要:
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 studiedM-estimators. Monte Carlo results suggest that the MHDE's are an attractive robust alternative to the usual sample means and covariance matrix.
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