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...

The estimation problem in multivariate linear calibration with elliptical errors is considered under a loss function which can be derived from the Kullback-Leibler distance. First, we discuss the problem under normal errors and we give unbiased estimate of risk of an alternative estimator by means of the Stein and Stein-Haff identities for multivariate normal distribution. From the unbiased est...

This paper applies the bootstrap methods proposed by Efron (1979) to the Stein variance estimator proposed by Stein (1964). It is shown by Monte Carlo experiments that the parametric bootstrap yields the considerable accurate estimates of mean, standard error and confidence limits of the Stein variance estimator.

An estimator in the extended class of Stein estimators has two undesirable properties. For a small value of prior guess, it ignores the data. Moreover, for some cases its risk is not uniformly smaller than that of Stein estimator. We show that there exists a lower bound on T(S) to guarantee a smaller risk, and the resulting estimator does not ignore the data.

This paper revisits the methodology of Stein (1975, 1986) for estimating a covariance matrix in the setting where the number of variables can be of the same magnitude as the sample size. Stein proposed to keep the eigenvectors of the sample covariance matrix but to shrink the eigenvalues. By minimizing an unbiased estimator of risk, Stein derived an ‘optimal’ shrinkage transformation. Unfortuna...

this comment is to show that theorem3.3 of dey and chung (1991) (multiparameter estimation intruncated power series distributions under the stein's loss.emph{commun. statist.-theory meth.,} {bf 20}, 309-326) may giveus misleading results. analytically and through simulation, weshow that the theorem does not improve the given estimator.

The paper presents the combined ridge-stein estimator (CRS) for linear pushbroom imagery exterior orientation, which is severely ill-conditioned for the strong correlation among exterior orientation elements of linear pushbroom imagery. The estimator is a new biased method combining the ridge estimator and the stein estimator. It can effectively change the ill-conditioned state of linear pushbr...

In this paper, we revisit the original ideas of Stein and propose an estimator of the intensity parameter of a homogeneous Poisson point process defined on Rd and observed on a bounded window. The procedure is based on a new integration by parts formula for Poisson point processes. We show that our Stein estimator outperforms the maximum likelihood estimator in terms of mean squared error. In m...