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.

In longitudinal imaging studies, geodesic regression in the space of diffeomorphisms [9] can be used to fit a generative model to images over time. The parameters of the model, primarily its initial direction or momentum, are important objects for study that contain biologically meaningful information about the dynamics occurring in the underlying anatomy. Unfortunately, it is common for any gi...

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...

We establish the Stein phenomenon in the context of two-step, monotone incomplete data drawn from Np+q(μ,Σ), a (p+ q)-dimensional multivariate normal population with mean μ and covariance matrix Σ. On the basis of data consisting of n observations on all p+q characteristics and an additional N − n observations on the last q characteristics, where all observations are mutually independent, denot...

This paper proposes a James-Stein-type (JS) adjustment to analytical bias correction in fixed effects panel models that suffer from the incidental parameters problem. We provide high-level conditions under which infeasible JS leads higher-order MSE improvement over bias-corrected estimator, and former is asymptotically equivalent latter. To obtain feasible adjustment, we propose nonparametric b...

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...

We study a general algorithm to improve accuracy in cluster analysis that employs the James-Stein shrinkage effect in k-means clustering. We shrink the centroids of clusters toward the overall mean of all data using a James-Stein-type adjustment, and then the James-Stein shrinkage estimators act as the new centroids in the next clustering iteration until convergence. We compare the shrinkage re...

The Stein estimator 's and the better positive-part Stein estimator gpS both dominate the sample mean, under quadratic loss, in the N(g, I) model of dimension q > 3. Standard large sample theory does not explaill this phenomenon well. Plausible bootstrap estimators for the risk of 's do not converge correctly at the shrinkage point as sample size n increases. By analyzing a submodel exactly, wi...