False Discovery Rates and the James-Stein Estimator

Estimation of the Multivariate Normal Mean under the Extended Reflected Normal Loss Function

Comparison of Small Area Estimation Methods for Estimating Unemployment Rate

Extended Abstract. In recent years, needs for small area estimations have been greatly increased for large surveys particularly household surveys in Sta­ tistical Centre of Iran (SCI), because of the costs and respondent burden. The lack of suitable auxiliary variables between two decennial housing and popula­ tion census is a challenge for SCI in using these methods. In general, the...

Supplementary Material to Kernel Mean Estimation and Stein Effect

Stein’s result has transformed common belief in statistical world that the maximum likelihood estimator, which is in common use for more than a century, is optimal. Charles Stein showed in 1955 that it is possible to uniformly improve the maximum likelihood estimator (MLE) for the Gaussian model in terms of total squared error risk when several parameters are estimated simultaneously from indep...

JAMES-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...

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...