In Statistical Theory
نویسنده
چکیده
Hajek's [17] convolution theorem was a major advance in understanding the classical information inequality. This re-examination of the convolution theorem discusses historical background to asymptotic estimation theory; the role of superefficiency in current estimation practice; the link between convergence of bootstrap distributions and convolution structure; and a dimensional asymptotics view of superefficiency.
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