James-Stein Type Estimators in Large Samples with Application to the Least Absolute Deviations Estimator
نویسندگان
چکیده
منابع مشابه
James-Stein Type Estimators in Large Samples with Application to the Least Absolute Deviations Estimator
We explore the extension of James-Stein type estimators in a direction that enables them to preserve their superiority when the sample size goes to infinity. Instead of shrinking a base estimator towards a fixed point, we shrink it towards a data-dependent point. We provide an analytic expression for the asymptotic risk and bias of James-Stein type estimators shrunk towards a data-dependent poi...
متن کاملJAMES-STEIN TYPE ESTIMATORS IN LARGE SAMPLES WITH APPLICATION TO THE LEAST ABSOLUTE DEVIATIONS ESTIMATOR BY TAE-HWAN KIM AND HALBERT WHITE DISCUSSION PAPER 99-04R MAY 2000 James-Stein Type Estimators in Large Samples with Application to the Least Absolute Deviations Estimator
We explore the extension of James-Stein type estimators in a direction that enables them to preserve their superiority when the sample size goes to infinity. Instead of shrinking a base estimator towards a fixed point, we shrink it towards a data-dependent point. We provide an analytic expression for the asymptotic risk and bias of James-Stein type estimators shrunk towards a data-dependent poi...
متن کاملJAMES-STEIN TYPE ESTIMATORS IN LARGE SAMPLES WITH APPLICATION TO THE LEAST ABSOLUTE DEVIATION ESTIMATOR BY TAE-HWAN KIM AND HALBERT WHITE DISCUSSION PAPER 99-04 FEBRUARY 1999 James-Stein Type Estimators in Large Samples with Application to The Least Absolute Deviation Estimator
We explore the extension of James-Stein type estimators in a direction that enables them to preserve their superiority when the sample size goes to infinity. Instead of shrinking a base estimator towards a fixed point, we shrink it towards a data-dependent point, which makes it possible that the “prior” becomes more accurate as the sample size grows. We provide an analytic expression for the as...
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This paper considers the problem of estimating a high-dimensional vector of parameters θ ∈ R from a noisy observation. The noise vector is i.i.d. Gaussian with known variance. For a squared-error loss function, the James-Stein (JS) estimator is known to dominate the simple maximum-likelihood (ML) estimator when the dimension n exceeds two. The JS-estimator shrinks the observed vector towards th...
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ژورنال
عنوان ژورنال: SSRN Electronic Journal
سال: 2001
ISSN: 1556-5068
DOI: 10.2139/ssrn.237016