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

Bernd Droge

Statistics & Probability Letters, 2006, vol. 76, issue 1, 83-92

Abstract: 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.

Keywords: Bounded; normal; mean; Soft; and; hard; thresholding; Minimax; regret; decision; theory; Nonlinear; estimation (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (2)

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