The Pullman meeting of IMS-WNAR had, as one of its themes, "Statistical consulting." In this overview of the case studies presented there, an attempt is made to draw together some of the lessons of these papers, showing the diverse role of the statistician in collecting, analyzing and presenting the information contained in the data.

In this paper, we describe a particular set of algorithms for clustering and show how they lead to codes which can be used to compress images. The approach is called tree-structured vector quantization (TSVQ) and amounts to a binary tree-structured two-means clustering, very much in the spirit of CART. This coding is thereafter put into the larger framework of information theory. Finally, we report the methodology for how image compression was applied in a clinical setting, where the medical issue was the measurement of major blood vessels in the chest and the technology was magnetic resonance (MR) imaging. Measuring the sizes of blood vessels, of other organs and of tumors is fundamental to evaluating aneurysms, especially prior to surgery. We argue for digital approaches to imaging in general, two benefits being improved archiving and transmission, and another improved clinical usefulness through the application of digital image processing. These goals seem particularly appropriate for technologies like MR that are inherently digital. However, even in this modern age, archiving the images of a busy radiological service is not possible without substantially compressing them. This means that the codes by which images are stored digitally, whether they arise from TSVQ or not, need to be "lossy," that is, not invertible. Since lossy coding necessarily entails the loss of digital information, it behooves those who recommend it to demonstrate that the quality of medicine practiced is not diminished thereby. There is a growing literature concerning the impact of lossy compression upon tasks that involve detection. However, we are not aware of similar studies of measurement. We feel that the study reported here of 30 scans compressed to 5 different levels, with measurements being made by 3 accomplished radiologists, is consistent with 16:1 lossy compression as we practice it being acceptable for the problem at hand.

A digitized handwritten numeral can be represented as a binary or greyscale image. An important pattern recognition task that has received much attention lately is to automatically determine the digit, given the image.

While many different techniques have been pushed very hard to solve this task, the most successful and intuitively appropriate is due to Simard, Le Cun and Denker (1993). Their approach combined nearest-neighbor classification with a subject-specific invariant metric that allows for small rotations, translations and other natural transformations. We report on Simard's classifier and compare it to other approaches. One important negative aspect of near-neighbor classification is that all the work gets done at lookup time, and with around 10,000 training images in high dimensions this can be exorbitant.

In this paper we develop rich models for representing large subsets of the prototypes. One example is a low-dimensional hyperplane defined by a point and a set of basis or tangent vectors. The components of these models are learned from the training set, chosen to minimize the average tangent distance from a subset of the training images--as such they are similar in flavor to the singular value decomposition (SVD), which finds closest hyperplanes in Euclidean distance. These models are either used singly per class or used as basic building blocks in conjunction with the $K$-means clustering algorithm.

The Iowa State Statistical Laboratory was established in 1933, with George W. Snedecor as director. The forces leading to this early creation of a formal unit are described, including the roles played by Henry A. Wallace and R. A. Fisher. Preceding this account, the state of statistics in 1933 is outlined, with special emphasis on U.S. universities. The lives and contributions of several leading personalities are sketched.

Geoffrey Stuart Watson, Professor Emeritus at Princeton University, celebrated his 75th birthday on December 3, 1996. A native Australian, his early education included Bendigo High School and Scotch College in Melbourne. After graduating with a B.A. (Hons.) from Melbourne University in December 1942, he spent the next few years, during and after World War II, doing research and teaching on applied mathematical topics. His wandering as a scholar began in 1947, when he became a graduate student in the Institute of Statistics in Raleigh, North Carolina. Leaving Raleigh after two years, he wrote his thesis while visiting the Department of Applied Economics in Cambridge University. Raleigh awarded him the Ph.D. degree in 1951.

That same year, he returned to Australia, to a Senior Lectureship in Statistics at Melbourne University. He moved in 1954 to a Senior Fellowship at the Australian National University. Three years later, he left for England and North America. In 1959, he became Associate Professor of Mathematics at the University of Toronto. In 1962, he became Professor of Statistics at The Johns Hopkins University in Baltimore. Soon thereafter he was appointed department chairman. In 1970, he moved to Princeton University as Professor and Chairman of Statistics. He became Professor Emeritus at Princeton in 1992.

He has published numerous research papers on a broad range of topics in statistics and applied probability. His best known contributions are the Durbin-Watson test for serial correlation, the Nadaraya-Watson estimator in nonparametric regression and fundamental methods for analyzing directional or axial data. He is the author of an important monograph, Statistics on Spheres. His professional honors include Membership in the International Statistical Institute and Fellowships of the Institute of Mathematical Statistics and of the American Association for the Advancement of Science. In private life, he is an accomplished painter of watercolors, a few of which may be seen on his website (http://www.princeton.edu/gsw/) at Princeton University. He married Shirley Elwyn Jennings in 1952. Their four children, one son and three daughters, pursue careers in Japanese literature, health care in Uganda, singing opera, and administering opera and ballet.