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Robust multivariate location estimation, admissibility, and shrinkage phenomenon
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Jana Jurečková
Published/Copyright:
September 25, 2009
Estimators of multivariate location parameters are generally dominated, in finite as well as asymptotic setups, by suitable shrinkage versions, and hence are inadmissible; such shrinkage estimators may not be admissible either. This feature is shared by maximum likelihood and many robust estimators. The interplay of robustness, admissibility and shrinkage phenomenon in some general multivariate location models (not necessarily elliptically or spherically symmetric) is illustrated and applied to Huber-type contamination models.
Keywords: Huber contamination model; M-estimation; posterior mean; score function; superharmonic function
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Received: 2005-July-29
Accepted: 2006-March-21
Published Online: 2009-09-25
Published in Print: 2006-12
© Oldenbourg Wissenschaftsverlag
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- Statistical inference on graphs
- Estimating market risk with neural networks
- On Markovian short rates in term structure models driven by jump-diffusion processes
- Robust multivariate location estimation, admissibility, and shrinkage phenomenon
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Keywords for this article
Huber contamination model;
M-estimation;
posterior mean;
score function;
superharmonic function
Articles in the same Issue
- Statistical inference on graphs
- Estimating market risk with neural networks
- On Markovian short rates in term structure models driven by jump-diffusion processes
- Robust multivariate location estimation, admissibility, and shrinkage phenomenon
- On local bootstrap bandwidth choice in kernel density estimation
- Correction note: On the optimal risk allocation problem
