Ann Cohen Brandwein

Shrinkage estimators of the location parameter for certain spherically symmetric distributions

We consider estimation of a location vector for particular subclasses of spherically symmetric distributions in the presence of a known or unknown scale parameter. Specifically, for these spherically symmetric distributions we obtain slightly more general conditions and larger classes of estimators than Brandwein and Strawderman (1991,Ann. Statist.,19, 1639–1650) under which estimators of the formX +ag(X) dominateX for quadratic loss, concave functions of quadratic loss and general quadratic loss.

Stein estimation for non-normal spherically symmetric location families in three dimensions

We consider estimation of a location vector in the presence of known or unknown scale parameter in three dimensions. The technique of proof is Steins integration by parts and it is used to cover several cases (e.g., non-unimodal distributions) for which previous results were known only in the cases of four and higher dimensions. Additionally, we give a necessary and sufficient condition on the shrinkage constant for improvement on the usual estimator for the spherical uniform distribution.

Stein Estimation for Spherically Symmetric Distributions: Recent Developments

This paper reviews advances in Stein-type shrinkage estimation for spherically symmetric distributions. Some emphasis is placed on developing intuition as to why shrinkage should work in location problems whether the underlying population is normal or not. Considerable attention is devoted to generalizing the “Stein lemma” which underlies much of the theoretical development of improved minimax estimation for spherically symmetric distributions. A main focus is on distributional robustness results in cases where a residual vector is available to estimate an unknown scale parameter, and, in particular, in finding estimators which are simultaneously generalized Bayes and minimax over large classes of spherically symmetric distributions. Some attention is also given to the problem of estimating a location vector restricted to lie in a polyhedral cone.

Using a Recognition and Reward Initiative to Improve Service Quality: A Quasi-Experimental Field Study in a Public Higher Education Institution

We describe an intervention undertaken to improve service quality in a public sector institution. More specifically, our service quality initiative focused on improving the work behavior and job attitudes of employees in a job category that is often overlooked, yet which is integral to the success of most public (and private) sector organizations—administrative assistants. In colleges and universities it has long been customary at the end of each academic year to recognize the outstanding achievements of faculty and students; however, administrative assistants have traditionally never received any accolades. A recognition and reward initiative for administrative assistants was implemented during the three academic years (2003/2004 through 2005/2006) as part of a service excellence initiative. Data on service quality were collected on an ongoing basis by an independent entity, Educational Benchmarks, Inc. After the third year of the recognition and reward initiative, survey data were also obtained from administrative assistants and individuals in related job titles. In general, attitudes were highly favorable (e.g., 89 percent of respondents wanted the program to continue), and numerous positive comments were provided such as “it is a good feeling to be recognized by your peers and commended for your work.” The present action research suggests that a recognition and reward intervention can improve service excellence in a public sector higher education organization. Further, we believe that the present intervention is transportable to various public sector entities.

Bayesian Estimation of Multivariate Location Parameters

This paper presents an expository development of Bayesian estimation with substantial emphasis on exact results for the multivariate normal location models with respect to squared error loss. From the time Stein, in 1956, showed the inadmissibility of the best invariant estimator when sampling from a multivariate normal distribution in 3 or more dimensions, there has been an outpouring of improved estimators with a Bayesian flavor, encouraged largely by the connections between Bayes estimation, admissibility and minimaxity. In this chapter, we attempt to give a coherent presentation of numerous Bayesian results (proper, generalized, empirical) for this case. Generalizations for the location parameter of multivariate normal distributions with unknown covariance matrices and general quadratic loss are also presented.

The Evaluation of a Peer Tutoring Program: A Quantitative Approach

Over the past several years, peer tutoring programs have been developed and utilized in higher education. Subjective studies have been made on the effectiveness of these programs, but no quantitative measures of effectiveness exist in the literature. We present a quantitative way of measuring peer tutor training programs and peer tutor effectiveness. Since so many peer tutoring programs are fashioned in a similar way, this quantitative measure will be easily adaptable to most situations.