A note on the bounded normal mean problem

Jianqing Fan, Jin Ting Zhang

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The bounded normal mean problem has important applications in nonparametric function estimation. It is to estimate the mean of a normal distribution with mean restricted to a bounded interval. The minimax risk for such a problem is generally unknown. It is shown in Donoho, Liu and MacGibbon (1990) that the linear minimax risk provides a good approximation to the minimax risk. We show in this note that a better approximation can be obtained by a simple truncation of the minimax linear estimator and that the minimax linear estimator is itself inadmissible. The gain of the truncated minimax linear estimator is significant for moderate size of the mean interval, where no analytical expression for the minimax risk is available. In particular, we show that the truncated minimax linear estimator performs no more than 13% worse than the minimax estimator, comparing with 25% for the minimax linear estimator.

Original languageEnglish (US)
Title of host publicationAdvances In Statistical Modeling And Inference
Subtitle of host publicationEssays In Honor Of Kjell A Doksum
PublisherWorld Scientific Publishing Co.
Pages635-647
Number of pages13
ISBN (Electronic)9789812708298
DOIs
StatePublished - Jan 1 2007
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Mathematics

Keywords

  • Bounded normal mean
  • Minimax risk
  • Quadratic loss

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