Shrinkage estimation under multivariate elliptic models. (English) Zbl 1319.62110
Summary: The estimation of the location vector of a \(p\)-variate elliptically contoured distribution (ECD) is considered using independent random samples from two multivariate elliptically contoured populations when it is apriori suspected that the location vectors of the two populations are equal. For the setting where the covariance structure of the populations is the same, we define the maximum likelihood, Stein-type shrinkage and positive-rule shrinkage estimators. The exact expressions for the bias and quadratic risk functions of the estimators are derived. The comparison of the quadratic risk functions reveals the dominance of the Stein-type estimators if \(p \geq 3\). A graphical illustration of the risk functions under a “typical” member of the elliptically contoured family of distributions is provided to confirm the analytical results.
MSC:
| 62H12 | Estimation in multivariate analysis |
| 62J07 | Ridge regression; shrinkage estimators (Lasso) |
| 62F10 | Point estimation |
Keywords:
bias and risk functions; elliptically contoured distributions; Hoteling’s \(T^2\) statistic; quadratic loss; Stein-type and positive-rule shrinkage estimatorsReferences:
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