Statistics

Alan E. Gelfand was born April 17, 1945, in the Bronx, New York. He attended public grade schools and did his undergraduate work at what was then called City College of New York (CCNY, now CUNY), excelling at mathematics. He then surprised and saddened his mother by going all the way across the country to Stanford to graduate school, where he completed his dissertation in 1969 under the direction of Professor Herbert Solomon, making him an academic grandson of Herman Rubin and Harold Hotelling. Alan then accepted a faculty position at the University of Connecticut (UConn) where he was promoted to tenured associate professor in 1975 and to full professor in 1980. A few years later he became interested in decision theory, then empirical Bayes, which eventually led to the publication of Gelfand and Smith [J. Amer. Statist. Assoc. 85 (1990) 398-409], the paper that introduced the Gibbs sampler to most statisticians and revolutionized Bayesian computing. In the mid-1990s, Alan's interests turned strongly to spatial statistics, leading to fundamental contributions in spatially-varying coefficient models, coregionalization, and spatial boundary analysis (wombling). He spent 33 years on the faculty at UConn, retiring in 2002 to become the James B. Duke Professor of Statistics and Decision Sciences at Duke University, serving as chair from 2007-2012. At Duke, he has continued his work in spatial methodology while increasing his impact in the environmental sciences. To date, he has published over 260 papers and 6 books; he has also supervised 36 Ph.D. dissertations and 10 postdocs. This interview was done just prior to a conference of his family, academic descendants, and colleagues to celebrate his 70th birthday and his contributions to statistics which took place on April 19-22, 2015 at Duke University.

Donald Bruce Rubin is John L. Loeb Professor of Statistics at Harvard University. He has made fundamental contributions to statistical methods for missing data, causal inference, survey sampling, Bayesian inference, computing and applications to a wide range of disciplines, including psychology, education, policy, law, economics, epidemiology, public health and other social and biomedical sciences.

Eugenio Regazzini was born on August 12, 1946 in Cremona (Italy), and took his degree in 1969 at the University "L. Bocconi" of Milano. He has held positions at the universities of Torino, Bologna and Milano, and at the University "L. Bocconi" as assistant professor and lecturer from 1974 to 1980, and then professor since 1980. He is currently professor in probability and mathematical statistics at the University of Pavia. In the periods 1989-2001 and 2006-2009 he was head of the Institute for Applications of Mathematics and Computer Science of the Italian National Research Council (C.N.R.) in Milano and head of the Department of Mathematics at the University of Pavia, respectively. For twelve years between 1989 and 2006, he served as a member of the Scientific Board of the Italian Mathematical Union (U.M.I.). In 2007, he was elected Fellow of the IMS and, in 2001, Fellow of the "Istituto Lombardo---Accademia di Scienze e Lettere." His research activity in probability and statistics has covered a wide spectrum of topics, including finitely additive probabilities, foundations of the Bayesian paradigm, exchangeability and partial exchangeability, distribution of functionals of random probability measures, stochastic integration, history of probability and statistics. Overall, he has been one of the most authoritative developers of de Finetti's legacy. In the last five years, he has extended his scientific interests to probabilistic methods in mathematical physics; in particular, he has studied the asymptotic behavior of the solutions of equations, which are of interest for the kinetic theory of gases. The present interview was taken in occasion of his 65th birthday.

George G. Roussas was born in the city of Marmara in central Greece, on June 29, 1933. He received a B.A. with high honors in Mathematics from the University of Athens in 1956, and a Ph.D. in Statistics from the University of California, Berkeley, in 1964. In 1964--1966, he served as Assistant Professor of Mathematics at the California State University, San Jose, and he was a faculty member of the Department of Statistics at the University of Wisconsin, Madison, in 1966--1976, starting as an Assistant Professor in 1966, becoming a Professor in 1972. He was a Professor of Applied Mathematics and Director of the Laboratory of Applied Mathematics at the University of Patras, Greece, in 1972--1984. He was elected Dean of the School of Physical and Mathematical Sciences at the University of Patras in 1978, and Chancellor of the university in 1981. He served for about three years as Vice President-Academic Affairs of the then new University of Crete, Greece, in 1981--1985. In 1984, he was a Visiting Professor in the Intercollege Division of Statistics at the University of California, Davis, and he was appointed Professor, Associate Dean and Chair of the Graduate Group in Statistics in the same university in 1985; he served in the two administrative capacities in 1985--1999. He is an elected member of the International Statistical Institute since 1974, a Fellow of the Royal Statistical Society since 1975, a Fellow of the Institute of Mathematical Statistics since 1983, and a Fellow of the American Statistical Association since 1986. He served as a member of the Council of the Hellenic Mathematical Society, and as President of the Balkan Union of Mathematicians.

The following conversation is partly based on an interview that took place in the Hong Kong University of Science and Technology in July 2013.

Jim Hannan is a professor who has lived an interesting life and one whose fundamental research in repeated games was not fully appreciated until late in his career. During his service as a meteorologist in the Army in World War II, Jim played poker and made weather forecasts. It is curious that his later research included strategies for repeated play that apply to selecting the best forecaster. James Hannan was born in Holyoke, Massachusetts on September 14, 1922. He attended St. Jerome's High School and in January 1943 received the Ph.B. from St. Michael's College in Colchester, Vermont. Jim enlisted in the US Army Air Force to train and serve as a meteorologist. This took him to army airbases in China by the close of the war. Following discharge from the army, Jim studied mathematics at Harvard and graduated with the M.S. in June 1947. To prepare for doctoral work in statistics at the University of North Carolina that fall, Jim went to the University of Michigan in the summer of 1947. The routine admissions' physical revealed a spot on the lung and the possibility of tuberculosis. This caused Jim to stay at Ann Arbor through the fall of 1947 and then at a Veterans Administration Hospital in Framingham, Massachusetts to have his condition followed more closely. He was discharged from the hospital in the spring and started his study at Chapel Hill in the fall of 1948. There he began research in compound decision theory under Herbert Robbins. Feeling the need for teaching experience, Jim left Chapel Hill after two years and short of thesis to take a three year appointment as an instructor at Catholic University in Washington, DC. When told that renewal was not coming, Jim felt pressure to finish his degree.

Jerome H. Friedman was born in Yreka, California, USA, on December 29, 1939. He received his high school education at Yreka High School, then spent two years at Chico State College before transferring to the University of California at Berkeley in 1959. He completed an undergraduate degree in physics in 1962 and a Ph.D. in high-energy particle physics in 1968 and was a post-doctoral research physicist at the Lawrence Berkeley Laboratory during 1968-1972. In 1972, he moved to Stanford Linear Accelerator Center (SLAC) as head of the Computation Research Group, retaining this position until 2006. In 1981, he was appointed half time as Professor in the Department of Statistics, Stanford University, remaining half time with his SLAC appointment. He has held visiting appointments at CSIRO in Sydney, CERN and the Department of Statistics at Berkeley, and has had a very active career as a commercial consultant. Jerry became Professor Emeritus in the Department of Statistics in 2007. Apart from some 30 publications in high-energy physics early in his career, Jerry has published over 70 research articles and books in statistics and computer science, including co-authoring the pioneering books Classification and Regression Trees and The Elements of Statistical Learning. Many of his publications have hundreds if not thousands of citations (e.g., the CART book has over 21,000). Much of his software is incorporated in commercial products, including at least one popular search engine. Many of his methods and algorithms are essential inclusions in modern statistical and data mining packages. Honors include the following: the Rietz Lecture (1999) and the Wald Lectures (2009); election to the American Academy of Arts and Sciences (2005) and the US National Academy of Sciences (2010); a Fellow of the American Statistical Association; Paper of the Year (JASA 1980, 1985; Technometrics 1998, 1992); Statistician of the Year (ASA, Chicago Chapter, 1999); ACM Data Mining Lifetime Innovation Award (2002), Emanuel & Carol Parzen Award for Statistical Innovation (2004); Noether Senior Lecturer (American Statistical Association, 2010); and the IEEE Computer Society Data Mining Research Contribution Award (2012).

Jon August Wellner was born in Portland, Oregon, in August 1945. He received his Bachelor's degree from the University of Idaho in 1968 and his PhD degree from the University of Washington in 1975. From 1975 until 1983 he was an Assistant Professor and Associate Professor at the University of Rochester. In 1983 he returned to the University of Washington, and has remained at the UW as a faculty member since that time. Over the course of a long and distinguished career, Jon has made seminal contributions to a variety of areas including empirical processes, semiparametric theory, and shape-constrained inference, and has co-authored a number of extremely influential books. He has been honored as the Le Cam lecturer by both the IMS (2015) and the French Statistical Society (2017). He is a Fellow of the IMS, the ASA, and the AAAS, and an elected member of the International Statistical Institute. He has served as co-Editor of Annals of Statistics (2001--2003) and Editor of Statistical Science (2010--2013), and President of IMS (2016--2017). In 2010 he was made a Knight of the Order of the Netherlands Lion. In his free time, Jon enjoys mountain climbing and backcountry skiing in the Cascades and British Columbia.

Martin Bradbury Wilk was born on December 18, 1922, in Montréal, Québec, Canada. He completed a B.Eng. degree in Chemical Engineering in 1945 at McGill University and worked as a Research Engineer on the Atomic Energy Project for the National Research Council of Canada from 1945 to 1950. He then went to Iowa State College, where he completed a this http URL. and a Ph.D. degree in Statistics in 1953 and 1955, respectively. After a one-year post-doc with John Tukey, he became Assistant Director of the Statistical Techniques Research Group at Princeton University in 1956--1957, and then served as Professor and Director of Research in Statistics at Rutgers University from 1959 to 1963. In parallel, he also had a 14-year career at Bell Laboratories, Murray Hill, New Jersey. From 1956 to 1969, he was in turn Member of Technical Staff, Head of the Statistical Models and Methods Research Department, and Statistical Director in Management Sciences Research. He wrote a number of influential papers in statistical methodology during that period, notably testing procedures for normality (the Shapiro--Wilk statistic) and probability plotting techniques for multivariate data. In 1970, Martin moved into higher management levels of the American Telephone and Telegraph (AT&T) Company. He occupied various positions culminating as Assistant Vice-President and Director of Corporate Planning. In 1980, he returned to Canada and became the first professional statistician to serve as Chief Statistician. His accomplishments at Statistics Canada were numerous and contributed to a resurgence of the institution's international standing. He played a crucial role in the reinstatement of the Cabinet-cancelled 1986 Census.

Nan McKenzie Laird is the Harvey V. Fineberg Professor of Biostatistics at the Harvard T. H. Chan School of Public Health. She has made fundamental contributions to statistical methods for longitudinal data analysis, missing data and meta-analysis. In addition, she is widely known for her work in statistical genetics and in statistical methods for psychiatric epidemiology. Her 1977 paper with Dempster and Rubin on the EM algorithm is among the top 100 most highly cited papers in science [Nature 524 (2014) 550-553]. Her applied work on medical practice errors is widely cited among the medical malpractice community. Nan was born in Gainesville, Florida, in 1943. Shortly thereafter, her parents Angus McKenzie Laird and Myra Adelia Doyle, moved to Tallahassee, Florida, with Nan and her sister Victoria Mell. Nan started college at Rice University in 1961, but then transferred to the University of Georgia where she received a B.S. in Statistics in 1969 and was elected to Phi Beta Kappa. After graduation Nan worked at the Massachusetts Institute of Technology Draper Laboratories where she worked on Kalman filtering for the Apollo Man to the Moon Program. She enrolled in the Statistics Department at Harvard University in 1971 and received her Ph.D. in 1975. She joined the faculty of Harvard School of Public Health upon receiving her Ph.D., and remains there as research professor, after her retirement in 2015. The interview was conducted in Boston, Massachusetts, in July 2014. A link to Nan's full CV can be found at \this http URL.