A Uniformly Asymptotically Efficient Estimator of a Location Parameter

Kei Takeuchi ()
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Kei Takeuchi: Professor Emeritus, The University of Tokyo

Chapter Chapter 6 in Contributions on Theory of Mathematical Statistics, 2020, pp 149-170 from Springer

Abstract: Abstract Suppose that a sample of size n from a continuous and symmetric population with an unknown location parameter is given. We consider a fictitious random subsample of size k drawn 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 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 or 20.

Date: 2020
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DOI: 10.1007/978-4-431-55239-0_6

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