Research funding and reputation in the UK have, for over two decades, been increasingly dependent on a regular peer-review of all UK departments. This is to move to a system more based on bibliometrics. Assessment exercises of this kind influence the behavior of institutions, departments and individuals, and therefore bibliometrics will have effects beyond simple measurement.

We discuss the paper “Citation Statistics” by the Joint Committee on Quantitative Assessment of Research. In particular, we focus on a necessary feature of “good” measures for ranking scientific authors: that good measures must able to accurately distinguish between authors.

A basic feature of many field experiments is that investigators are only able to randomize clusters of individuals—such as households, communities, firms, medical practices, schools or classrooms—even when the individual is the unit of interest. To recoup the resulting efficiency loss, some studies pair similar clusters and randomize treatment within pairs. However, many other studies avoid pairing, in part because of claims in the literature, echoed by clinical trials standards organizations, that this matched-pair, cluster-randomization design has serious problems. We argue that all such claims are unfounded. We also prove that the estimator recommended for this design in the literature is unbiased only in situations when matching is unnecessary; its standard error is also invalid. To overcome this problem without modeling assumptions, we develop a simple design-based estimator with much improved statistical properties. We also propose a model-based approach that includes some of the benefits of our design-based estimator as well as the estimator in the literature. Our methods also address individual-level noncompliance, which is common in applications but not allowed for in most existing methods. We show that from the perspective of bias, efficiency, power, robustness or research costs, and in large or small samples, pairing should be used in cluster-randomized experiments whenever feasible; failing to do so is equivalent to discarding a considerable fraction of one’s data. We develop these techniques in the context of a randomized evaluation we are conducting of the Mexican Universal Health Insurance Program.

In 1952 Lucien Le Cam announced his celebrated result that, for regular univariate statistical models, sets of points of superefficiency have Lebesgue measure zero. After reviewing the turbulent history of early studies of superefficiency, I suggest using the notion of computability as a tool for exploring the phenomenon of superefficiency. It turns out that only computable parameter points can be points of superefficiency for computable estimators. This algorithmic version of Le Cam’s result implies, in particular, that sets of points of superefficiency not only have Lebesgue measure zero but are even countable.

Willem Rutger van Zwet was born in Leiden, the Netherlands, on March 31, 1934. He received his high school education at the Gymnasium Haganum in The Hague and obtained his Masters degree in Mathematics at the University of Leiden in 1959. After serving in the army for almost two years, he obtained his Ph.D. at the University of Amsterdam in 1964, with Jan Hemelrijk as advisor. In 1965, he was appointed Associate Professor of Statistics at the University of Leiden and promoted to Full Professor in 1968. He remained in Leiden until his retirement in 1999, while also serving as Associate Professor at the University of Oregon (1965), William Newman Professor at the University of North Carolina at Chapel Hill (1990–1996), frequent visitor and Miller Professor (1997) at the University of California at Berkeley, director of the Thomas Stieltjes Institute of Mathematics in the Netherlands (1992–1999), and founding director of the European research institute EURANDOM (1997–2000). At Leiden, he was Dean of the School of Mathematics and Natural Sciences (1982–1984). He served as chair of the scientific council and member of the board of the Mathematics Centre at Amsterdam (1983–1996) and the Leiden University Fund (1993–2005).

Bill served on numerous committees of the Institute of Mathematical Statistics (IMS), the Bernoulli Society for Mathematical Statistics and Probability (BS), the International Statistical Institute (ISI) and the American Statistical Association (ASA). For IMS, he was Associate Editor (1972–1980) and Editor (1986–1988) of The Annals of Statistics, and President (1991–1992). For the Bernoulli Society, he was President (1987–1989) and Editor-in-Chief of Bernoulli (2000–2003). He served ISI as Chair of the Organizing Committee of the Centenary Session at Amsterdam (1985), two-term Vice-President (1985–1989), program chair for the Session at Florence (1993), and President (1997–1999). He was a member of the Board of Directors of ASA (1993–1995). He was a member of the Corporation and the Board of NISS (1993–2002). He served as member and chair of the European Regional Committee (1969–1980) that organized the European Meetings of Statisticians, and for many years as an organizer of the Dutch Stochastics Meetings at Lunteren (1972–1999).

He gave the Wald Memorial Lectures (1992), the Hotelling Lectures at the University of North Carolina (1988), and the Bahadur Lectures at the University of Chicago (2005). He is an Honorary Doctor of Charles University at Prague (1997), a member of the Royal Netherlands Academy of Sciences (1979), and the Academia Europaea (1990). He is a recipient of the Van Dantzig Medal of the Netherlands Society for Statistics and Operations Research (1970), the Bernoulli Medal (Tashkent, 1986), the Peace Medal of Charles University (1988), the AKZO-Nobel Award (1996), and the Alexander von Humboldt Research Prize (2006). In 1996, he was made a Knight in the Order of the Netherlands Lion by Queen Beatrix of the Netherlands.

Bill has published some 80 research papers and two books.

On an exquisite March day in 2006, David Brillinger and Richard Davis sat down with Murray and Ady Rosenblatt at their home in La Jolla, California for an enjoyable day of reminiscences and conversation. Our mentor, Murray Rosenblatt, was born on September 7, 1926 in New York City and attended City College of New York before entering graduate school at Cornell University in 1946. After completing his Ph.D. in 1949 under the direction of the renowned probabilist Mark Kac, the Rosenblatts’ moved to Chicago where Murray became an instructor/assistant professor in the Committee of Statistics at the University of Chicago. Murray’s academic career then took him to the University of Indiana and Brown University before his joining the University of California at San Diego in 1964. Along the way, Murray established himself as one of the most celebrated and leading figures in probability and statistics with particular emphasis on time series and Markov processes. In addition to being a fellow of the Institute of Mathematical Statistics and American Association for the Advancement of Science, he was a Guggenheim fellow (1965–1966, 1971–1972) and was elected to the National Academy of Sciences in 1984. Among his many contributions, Murray conducted seminal work on density estimation, central limit theorems under strong mixing, spectral domain methods and long memory processes. Murray and Ady Rosenblatt were married in 1949 and have two children, Karin and Daniel.