A Uniformly Asymptotically Efficient Estimator of a Location Parameter
作者:
Kei Takeuchi,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1971)
卷期:
Volume 66,
issue 334
页码: 292-301
ISSN:0162-1459
年代: 1971
DOI:10.1080/01621459.1971.10482258
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
Suppose that a sample of size n from a continuous and symmetric population with an unknown parameter is given. We consider a fictitious random subsample of sizekdrawn from the original sample and construct the best linear estimator based on the subsample. Applying the Rao-Blackwell type argument, we get an estimator which uses the information contained in the whole sample and is supposed to be uniformly efficient for a wide class of distributions. Monte Carlo experiments established that this estimator is highly efficient for small samples of size 10 to 20.
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