Robust Improvement in Estimation of a Mean Matrix in an Elliptically Contoured Distribution
By Tatsuya Kubokawa, Muni S. Srivastava. 2001
47 citations recorded by the source corpus.
In estimation of a matrix of regression coefficients in a multivariate linear regression model, this paper shows that minimax and shrinkage estimators under a normal distribution remain robust under an elliptically contoured distribution. The robustness of the improvement is established for both invariant and noninvariant loss functions in the above model as well as in the growth curve model.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/19e782ab4358e75d93205fc16c2ab30fb28b8ae8
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In Statistics and Probability
- Stéphane Canu
- Dominique Fourdrinier
- Martin T. Wells
- Aurélie Boisbunon
- William E. Strawderman
- Yuzo Maruyama
- Christian Genest
- Mark F. J. Steel
Citations in the corpus
- Akaike's Information Criterion, Cp and Estimators of Loss for Elliptically Symmetric Distributions (2014, background): “Kubokawa & Srivastava (2001) addressed the multivariate case where is a mean matrix (in this case, Z and U are matrices as well); we will introduce this case in Section 4.2.”
- A new class of generalized Bayes minimax ridge regression estimators (2004, methodology): “We use the version derived in [13], but earlier versions appear in [16] and elsewhere.”