Limit expressions for the risk of james-stein estimators

Limit expressions (as the dimension p ← ∞ ) are derived for the relative risk of the James-Stein estimator and its positive-part version. The limit is simple to evaluate, and gives the amount of improvement in risk that is possible. The technique used is to bound the risk, both above and below. with bounds that converge to the same limit. For the James-Stein estimator these bounds are simple to calculate, and are quite accurate even for moderate dimensions.     

Universal domination of stein-type estimators

For the problem of estimating the location parameter of a p-variate spherically symmetric distribution (p>3), Hwang (1985) established the dominance of some positive-part James-Stein (1961) estimators over the usual estimator simultaneously under a very general class of loss function. Vie show that many of his results can be extended to a class of positive-part Baranchik-type estimators (1970).     

A lower bound for the risk of classes of shrinkage estimators ina general multivariate estimation problem and some deduced estimators

Given a general statistical model and an arbitrary quadratic loss, we propose a lower bound for the associated risk of a class of shrinkage estimators. With respect to the considered class of shrinkage estimators, this bound is optimal.In the particular case of the estimation of the location parameter of an ellipti-cally symmetric distribution, this bound can be used to find the relative improvement brought by a given estimator and the remaining possible improvement, using a Monte-Carlo method. We deduce from these results a new type of shrinkage estimators whose risk can be as close as one wants of the lower bound near a chosen pole and yet remain bounded. Some of them are good alternatives to the positive-part James-Stein estimator.     

A non stochastic ridge regression estimator and comparison with the James-Stein estimator

Abstract

This article presents a non-stochastic version of the Generalized Ridge Regression estimator that arises from a discussion of the properties of a Generalized Ridge Regression estimator whose shrinkage parameters are found to be close to their upper bounds. The resulting estimator takes the form of a shrinkage estimator that is superior to both the Ordinary Least Squares estimator and the James-Stein estimator under certain conditions. A numerical study is provided to investigate the range of signal to noise ratio under which the new estimator dominates the James-Stein estimator with respect to the prediction mean square error.     

A note on james-stein and bayes empiricl bayes estimators

In an empirical Bayes decision problem, a simple class of estimators is constructed that dominate the James-Stein

estimator, A prior distribution A is placed on a restricted (normal) class G of priors to produce a Bayes empirical Bayes estimator, The Bayes empirical Bayes estimator is smooth, admissible, and asymptotically optimal. For certain A rate of convergence to minimum Bayes risk is 0(n^-1)uniformly on G. The results of a Monte Carlo study are presented to demonstrate the favorable risk bebhavior of the Bayes estimator In comparison with other competitors including the James-Stein estimator.     

Improved set estimators for the coefficients of a linear model when the error distribution is spherically symmetric with unknown variances

The usual confidence set for p (p ≥ 3) coefficients of a linear model is known to be dominated by the James-Stein confidence sets under the assumption of spherical symmetric errors with known variance (Hwang and Chen 1986). For the same confidence-set problem but for the unknown-variance case, naturally one replaces the unknown variance by an estimator. For the normal case, many previous studies have shown numerically that the resultant James-Stein confidence sets dominate the resultant usual confidence sets, i.e., the F confidence sets. In this paper we provide a further asymptotic justification, and we discover the same advantage of the James-Stein confidence sets for normal error as well as spherically symmetric error.     

PMSE dominance of the positive-part shrinkage estimator in a regression model with proxy variables

Consider a linear regression model with some relevant regressors are unobservable. In such a situation, we estimate the model by using the proxy variables as regressors or by simply omitting the relevant regressors. In this paper, we derive the explicit formula of predictive mean squared error (PMSE) of a general family of shrinkage estimators of regression coefficients. It is shown analytically that the positive-part shrinkage estimator dominates the ordinary shrinkage estimator even when proxy variables are used in place of the unobserved variables. Also, as an example, our result is applied to the double k-class estimator proposed by Ullah and Ullah (Double k-class estimators of coefficients in linear regression. Econometrica. 1978;46:705–722). Our numerical results show that the positive-part double k-class estimator with proxy variables has preferable PMSE performance.     

MSE performance of the 2SHI estimator in a regression model with multivariate t error terms

t error terms and derive the explicit formula of the mean squared error (MSE) of the two-stage hierarchial information (2SHI) estimator. It is shown by numerical evaluations that the 2SHI estimator has smaller MSE than the positive-part Stein-rule (PSR) estimator over a wide region of the parameter space. Received: November 6, 1998; revised version: October 15, 1999     

Reference prior bayes estimator for bivariate normal covariance matrix with risk comparison

The explicit form of the reference prior bayes estimator due to Yang and Ber-ger (1994) for bivariate normal covariance matrix under entropy loss is given in terms of Legendre polynomials when degrees of freedom is even and in terms of hypergeometric functions in general case. The finite series expression of the density function of the ratio of latent roots of bivariate Wishart matrix is obtained and the exact risk is compared with those of James-Stein minimax estimator and other orthogonally equivariant estimators. It is found numerically that the reference prior bayes estimator has the smallest risk among the class of equivariant estimators compared, when the ratio of the largest to the smallest population latent roots of covariance matrix lies in the middle of the interval [1, ∞]. It has larger risk than that of James-Stein minimax estimator when the ratio is large. Moreover it has larger risk than that of MLE when, for instance, degrees of freedom is 20 and the ratio lies between 4 and 8.     

A new estimator of covariance matrix

The problem of estimating a covariance matrix is considered in this paper. Using the so-called partial Iwasawa coordinates of the covariance matrix, a new improved estimator dominating the James-Stein estimator is proposed. The results of a simulation study verifies that the new estimator provides a substantial improvement in risk under Stein's loss.     

James-stein estimation with constraints on the norm

Consider the problem of estimating a multivariate mean 0(pxl), p>3, based on a sample x^ ..., xn with quadratic loss function. We find an optimal decision rule within the class of James-Stein type decision rules when the underlying distribution is that of a variance mixture of normals and when the norm ||0|| is known. When the norm is restricted to a known interval, typically no optimal James-Stein type rule exists but we characterize a minimal complete class within the class of James-Stein type decision rules. We also characterize the subclass of James-Stein type decision rules that dominate the sample mean.     

On an adjustment of degrees of freedom in the minimim mean squared error ertimator

In this paper, we consider an adjustment of degrees of freedom in the minimum mean squared error (MMSE) estimator, We derive the exact MSE of the adjusted MMSE (AMMSE) estimator, and compare the MSE of the AMMSE estimator with those of the Stein-(SR), positive-part Stein-rule (PSR) and MMSE estimators by numerical evaluations. It is shown that the adjustment of degrees of freedom is effective when the noncentrality parameter is close to zero, and the MSE performance of the MMSE estimator can be improved in the wide region of the noncentrality parameter by the adjustment, ft is also shown that the AMMSE estimator can have the smaller MSE than the PSR estimator in the wide region of the noncentrality parameter     

On unbiased and improved loss estimation for the mean of a multivariate normal distribution with unknown variance

There is now a sizeable literature dealing with point estimation using Stein-type estimators. As discussed in Rukhin (In: Gupta, S.S., Berger, J.O. (Eds.), Statistical Decision Theory and Related Topics, Vol. IV, Springer, New York, pp. 409–418), instances arise in practice in which an estimation rule is to be accompanied by an estimate of its loss, which is unobservable. In the context of estimating the mean vector of a multi-normal distribution assuming a known population variance, Johnstone (In: Gupta, S.S., Berger, J.O. (Eds.), Statistical Decision Theory and Related Topics, Vol. IV, Springer, New York, pp. 361–379) derived an estimator that dominates the unbiased estimator of the quadratic loss incurred by the James–Stein estimator. By applying the Stein's lemma, this note generalizes Johnstone's (In: Gupta, S.S., Berger, J.O. (Eds.), Statistical Decision Theory and Related Topics, Vol. IV, Springer, New York, pp. 361–379) analysis to the setting of the unknown population variance. Computational evidence is provided about the risk magnitude of loss estimators associated with the James–Stein point estimator and its positive-part version.     

MSE performance of the weighted average estimators consisting of shrinkage estimators

In this paper, we consider a regression model and propose estimators which are the weighted averages of two estimators among three estimators; the Stein-rule (SR), the minimum mean squared error (MMSE), and the adjusted minimum mean-squared error (AMMSE) estimators. It is shown that one of the proposed estimators has smaller mean-squared error (MSE) than the positive-part Stein-rule (PSR) estimator over a moderate region of parameter space when the number of the regression coefficients is small (i.e., 3), and its MSE performance is comparable to the PSR estimator even when the number of the regression coefficients is not so small.     

Improved estimation for the parameters of an inverse gaussian distribution

James-Stein estimators are proposed for the #-parameter of an inverse Gaussian #G# distribution. The estimators of this class have smaller expected quadratic loss than the maximum likelihood estimator usually employed when analysing real sets of data. This problem is also studied for the case of an unknown nuisance parameter. Finally, improved estimators are considered for # in the two sample problem.     

A james-stein type detour of U-statistics

For a vector of estimable parameters, a modified version of the James-Stein rule (incorporating the idea of preliminary test estimators) is utilized in formulating some estimators based on U-statistics and their jackknifed estimator of dispersion matrix. Asymptotic admissibility properties of the classical U-statistics, their preliminary test version and the proposed estimators are studied.     

Estimation of Several Intraclass Correlation Coefficients

An intraclass correlation coefficient observed in several populations is estimated. The basis is a variance-stabilizing transformation. It is shown that the intraclass correlation coefficient from any elliptical distribution should be transformed in the same way. Four estimators are compared. An estimator where the components in a vector consisting of the transformed intraclass correlation coefficients are estimated separately, an estimator based on a weighted average of these components, a pretest estimator where the equality of the components is tested and then the outcome of the test is used in the estimation procedure, and a James-Stein estimator which shrinks toward the mean.     

Finite sample moments of a bootstrap estimator of the james-stein rule

The finite sample moments of the bootstrap estimator of the James-Stein rule are derived and shown to be biased. Analytical results shed some light upon the source of bias and suggest that the bootstrap will be biased in other settings where the moments of the statistic of interest depends on nonlinear functions of the parameters of its distribution.     

Finite sample moments of a bootstrap estimator of the james-stein rule

The finite sample moments of the bootstrap estimator of the James-Stein rule are derived and shown to be biased. Analytical results shed some light upon the source of bias and suggest that the bootstrap will be biased in other settings where the moments of the statistic of interest depends on nonlinear functions of the parameters of its distribution.     

Dominating james-stein positive-part estimator for normal mean with unknown covariance matrix

Assume that we have a random sample of size n from p-variate normal population and we wish to estimate the mean vector under quadratic loss with respect to the inverse of the unknown covariance matrix, A class of superior estimators to James-Stein positive part estimator is given when n>max{9p+10,13p-7}, based on the argument by Shao and Strawderman(1994).