Minimax regret comparison of hard and soft thresholding for estimating a bounded normal mean

We study the problem of estimating the mean of a normal distribution with known variance, when prior knowledge specifies that this mean lies in a bounded interval. The focus is on a minimax regret comparison of soft and hard threshold estimators, which have become very popular in the context of wavelet estimation. Under squared-error loss it turns out that soft thresholding is superior to hard thresholding.

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Year of publication:	2006
Authors:	Droge, Bernd
Published in:	Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 76.2006, 1, p. 83-92
Publisher:	Elsevier
Keywords:	Bounded normal mean Soft and hard thresholding Minimax regret decision theory Nonlinear estimation

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005223885