We propose a Bayesian formulation of the sample size problem for planning clinical trials. The frequentist paradigm for calculating sample sizes for clinical trials is to prespecify the type I and II error probabilities. These error probabilities are conditional on the true hypotheses. Instead we propose prespecifying posterior probabilities which are conditional on the outcome of the trial. Our method is easy to implement and has intuitive interpretations. We illustrate an application of our method to the planning of cancer clinical trials for the Eastern Cooperative Oncology Group (ECOG).

Data mining is defined as the process of seeking interesting or valuable information within large data sets. This presents novel challenges and problems, distinct from those typically arising in the allied areas of statistics, machine learning, pattern recognition or database science. A distinction is drawn between the two data mining activities of model building and pattern detection. Even though statisticians are familiar with the former, the large data sets involved in data mining mean that novel problems do arise. The second of the activities, pattern detection, presents entirely new classes of challenges, some arising, again, as a consequence of the large sizes of the data sets. Data quality is a particularly troublesome issue in data mining applications, and this is examined. The discussion is illustrated with a variety of real examples.

Advancing computer technology is allowing us to downplay instruction in mechanical procedures and shift emphasis towards teaching the “art” of statistics. This paper is based upon interviews with six professional statisticians about statistical thinking and statistical practice. It presents themes emerging from their professional experience, emphasizing dimensions that were surprising to them and were not part of their statistical training. Emerging themes included components of sta­ tistical thinking, pointers to good statistical practices and the subtleties of interacting with the thinking of others, particularly coworkers and clients. The main purpose of the research is to uncover basic elements of applied statistical practice and statistical thinking for the use of teachers of statistics.

A topic of major current interest in extreme­value analysis is the investigation of temporal trends. For example, the potential influence of “greenhouse” effects may result in severe storms becoming gradually more frequent, or in maximum temperatures gradually increasing, with time. One approach to evaluating these possibilities is to fit, to data, a parametric model for temporal parameter variation, as well as a model describing the marginal distribution of data at any given point in time. However, structural trend models can be difficult to formulate in many circumstances, owing to the complex way in which different factors combine to influence data in the form of extremes. Moreover, it is not advisable to fit trend models without empirical evidence of their suitability. In this paper, motivated by datasets on windstorm severity and maximum temperature, we suggest a nonparametric approach to estimating temporal trends when fitting parametric models to extreme values from a weakly dependent time series. We illustrate the method through applications to time series where the marginal distributions are approximately-Pareto, generalized­Pareto, extreme­value or Gaussian. We introduce time­varying probability plots to assess goodness of fit, we discuss local­likelihood approaches to fitting the marginal model within a window and we propose temporal cross­validation for selecting window width. In cases where both location and scale are estimated together, the Gaussian distribution is shown to have special features that permit it to playa universal role as a “nominal” model for the marginal distribution.

Milton Sobel was born in New York City on August 30, 1919. He earned his B.A. degree in mathematics from the City College of New York in 1940, an M.A. degree in mathematics and a Ph.D. degree in mathematical statistics from Columbia University in 1946 and 1951, respectively. His Ph.D. thesis advisor was Abraham Wald. He has made substantial contributions in several areas of statistics and mathematics—including decision theory, sequential analysis, selection and ranking, reliability analysis, combinatorial problems, Dirichlet processes, as well as statistical tables and computing. He has been particularly credited for path breaking contributions in selection and ranking, sequential analysis and reliability, includingthe landmark book, Sequential Identi fication and Ranking Procedures (1968), coauthored with Robert E. Bechhofer and Jack C. Kiefer. Later, he collaborated with Jean D.Gibbons and Ingram Olkin to write a methodologically oriented book, Selecting and Ordering Populations (1977), on the subject. He has published authoritative books on Dirichlet distributions, Type 1 and Type 2 with V. R. R.Uppuluri and K. Frankowski. He is the author or coauthor of more than one hundred and twenty research publications, many of which are part of today ’s statistical folklore. During the period July 1940 through June 1960, his career path led him to work at the Census Bureau, the Army War College (Fort McNair),Columbia University, Wayne State University, Cornell University and Bell Laboratories. From September 1960 through June 1975, he was Professor of Statistics at the University of Minnesota, and from July 1975 through June 1989 he was a Professor in the Department of Probability and Statistics at the University of California at Santa Barbara. He has since been a Professor Emeritus at UC Santa Barbara. He has earned many honors and awards, including Fellow of the Institute of Mathematical Statistics (1956) and Fellow of the American Statistical Association (1958),a Guggenheim Fellowship (1967 –1968), a NIH Fellowship (1968 –1969)and elected membership in the International Statistical Institute (1974). He continues to think and work harder than many half his age and still goes to his department at UC Santa Barbara every day. Milton Sobel remains vigorous in attacking and solving hard problems.