Statistics

Nancy Flournoy was born in Long Beach, California, on May 4, 1947. After graduating from Polytechnic School in Pasadena in 1965, she earned a B.S. (1969) and M.S. (1971) in biostatistics from UCLA. Between her bachelors and masters degrees, she worked as a Statistician I for Regional Medical Programs at UCLA. After receiving her master's degree, she spend three years at the Southwest Laboratory for Education Research and Development in Seal Beach, California. Flournoy joined the Seattle team pioneering bone marrow transplantation in 1973. She moved with the transplant team into the newly formed Fred Hutchinson Cancer Research Center in 1975 as Director of Clinical Statistics, where she supervised a group responsible for the design and analysis of about 80 simultaneous clinical trials. To support the Clinical Division, she supervised the development of an interdisciplinary shared data software system. She recruited Leonard B. Hearne to create this database management system in 1975 (and married him in 1978). While at the Cancer Center, she was also at the University of Washington, where she received her doctorate in biomathematics in 1982. She became the first female director of the program in statistics at the National Science Foundation (NSF) in 1986. She received service awards from the NSF in 1988 and the National Institute of Statistical Science in 2006 for facilitating interdisciplinary research. Flournoy joined the Department of Mathematics and Statistics at American University in 1988. She moved as department chair to the University of Missouri in 2002, where she became Curators' Distinguished Professor in 2012.

Tadeusz Caliński was born in Poznań, Poland in 1928. Despite the absence of formal secondary eduction for Poles during the Second World War, he entered the University of Poznań in 1948, initially studying agronomy and in later years mathematics. From 1953 to 1988 he taught statistics, biometry and experimental design at the Agricultural University of Poznań. During this period he founded and developed the Poznań inter-university school of mathematical statistics and biometry, which has become one of the most important schools of this type in Poland and beyond. He has supervised 24 Ph.D. students, many of whom are currently professors at a variety of universities. He is now Professor Emeritus. Among many awards, in 1995 Professor Caliński received the Order of Polonia Restituta for his outstanding achievements in the fields of Education and Science. In 2012 the Polish Statistical Society awarded him The Jerzy Spława-Neyman Medal for his contribution to the development of research in statistics in Poland. Professor Caliński in addition has Doctoral Degrees honoris causa from the Agricultural University of Poznań and the Warsaw University of Life Sciences. His research interests include mathematical statistics and biometry, with applications to agriculture, natural sciences, biology and genetics. He has published over 140 articles in scientific journals as well as, with Sanpei Kageyama, two important books on the randomization approach to the design and analysis of experiments. He has been extremely active and successful in initiating and contributing to fruitful international research cooperation between Polish statisticians and biometricians and their colleagues in various countries, particularly in the Netherlands, France, Italy, Great Britain, Germany, Japan and Portugal. The conversations in addition cover the history of biometry and experimental design in Poland and the early influence of British statisticians.

Richard Olshen was born in Portland, Oregon, on May 17, 1942. Richard spent his early years in Chevy Chase, Maryland, but has lived most of his life in California. He received an A.B. in Statistics at the University of California, Berkeley, in 1963, and a Ph.D. in Statistics from Yale University in 1966, writing his dissertation under the direction of Jimmie Savage and Frank Anscombe. He served as Research Staff Statistician and Lecturer at Yale in 1966-1967. Richard accepted a faculty appointment at Stanford University in 1967, and has held tenured faculty positions at the University of Michigan (1972-1975), the University of California, San Diego (1975-1989), and Stanford University (since 1989). At Stanford, he is Professor of Health Research and Policy (Biostatistics), Chief of the Division of Biostatistics (since 1998) and Professor (by courtesy) of Electrical Engineering and of Statistics. At various times, he has had visiting faculty positions at Columbia, Harvard, MIT, Stanford and the Hebrew University. Richard's research interests are in statistics and mathematics and their applications to medicine and biology. Much of his work has concerned binary tree-structured algorithms for classification, regression, survival analysis and clustering. Those for classification and survival analysis have been used with success in computer-aided diagnosis and prognosis, especially in cardiology, oncology and toxicology. He coauthored the 1984 book Classification and Regression Trees (with Leo Brieman, Jerome Friedman and Charles Stone) which gives motivation, algorithms, various examples and mathematical theory for what have come to be known as CART algorithms. The approaches to tree-structured clustering have been applied to problems in digital radiography (with Stanford EE Professor Robert Gray) and to HIV genetics, the latter work including studies on single nucleotide polymorphisms, which has helped to shed light on the presence of hypertension in certain subpopulations of women.

Robert C. Elston was born on February 4, 1932, in London, England. He went to Cambridge University to study natural science from 1952-1956 and obtained B.A., M.A. and Diploma in Agriculture (Dip Ag). He came to the US at age 24 to study animal breeding at Cornell University and received his Ph.D. in 1959. From 1959-1960, he was a post-doctoral fellow in biostatistics at University of North Carolina (UNC), Chapel Hill, where he studied mathematical statistics. He then rose through the academic ranks in the department of biostatistics at UNC, becoming a full professor in 1969. From 1979-1995, he was a professor and head of the Department of Biometry and Genetics at Louisiana State University Medical Center in New Orleans. In 1995, he moved to Case Western Reserve University where he is a professor of epidemiology and biostatistics and served as chairman from 2008 to 2014. Between 1966 and 2013, he directed 42 Ph.D. students and mentored over 40 post-doctoral fellows. If one regards him as a founder of a pedigree in research in genetic epidemiology, it was estimated in 2007 that there were more than 500 progeny. Among his many honors are a NIH Research Career Development Award (1966-1976), the Leadership Award from International Society of Human Genetics (1995), William Allan Award from American Society of Human Genetics (1996), NIH MERIT Award (1998) and the Marvin Zelen Leadership Award, Harvard University (2004). He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics as well as a Fellow of the Ohio Academy of Science. A leader in research in genetic epidemiology for over 40 years, he has published over 600 research articles in biostatistics, genetic epidemiology and applications. He has also coauthored and edited 9 books in biostatistics, population genetics and methods for the analysis of genetic data.

The following conversation is based in part on a transcript of a 2009 interview funded by Pfizer Global Research-Connecticut, the American Statistical Association and the Department of Statistics at the University of Connecticut-Storrs as part of the "Conversations with Distinguished Statisticians in Memory of Professor Harry O. Posten".

Data science is an emerging interdisciplinary field that combines elements of mathematics, statistics, computer science, and knowledge in a particular application domain for the purpose of extracting meaningful information from the increasingly sophisticated array of data available in many settings. These data tend to be non-traditional, in the sense that they are often live, large, complex, and/or messy. A first course in statistics at the undergraduate level typically introduces students with a variety of techniques to analyze small, neat, and clean data sets. However, whether they pursue more formal training in statistics or not, many of these students will end up working with data that is considerably more complex, and will need facility with statistical computing techniques. More importantly, these students require a framework for thinking structurally about data. We describe an undergraduate course in a liberal arts environment that provides students with the tools necessary to apply data science. The course emphasizes modern, practical, and useful skills that cover the full data analysis spectrum, from asking an interesting question to acquiring, managing, manipulating, processing, querying, analyzing, and visualizing data, as well communicating findings in written, graphical, and oral forms.

How should we update de dicto beliefs in the face of de se evidence? The Sleeping Beauty problem divides philosophers into two camps, halfers and thirders. But there is some disagreement among halfers about how their position should generalize to other examples. A full generalization is not always given; one notable exception is the Halfer Rule, under which the agent updates her uncentered beliefs based on only the uncentered part of her evidence. In this brief article, I provide a simple example for which the Halfer Rule prescribes credences that, I argue, cannot be reasonably held by anyone. In particular, these credences constitute an egregious violation of the Reflection Principle. I then discuss the consequences for halfing in general.

In the context of the Sleeping Beauty problem, it has been argued that so-called "halfers" can avoid Dutch book arguments by adopting evidential decision theory. I introduce a Dutch book for a variant of the Sleeping Beauty problem and argue that evidential decision theorists fall prey to it, whether they are halfers or thirders. The argument crucially requires that an action can provide evidence for what the agent would do not only at other decision points where she has exactly the same information, but also at decision points where she has different but "symmetric" information.

A fast matching pursuit method using a Bayesian approach is introduced for sparse signal recovery. This method, referred to as nGpFBMP, performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian or unknown. It is agnostic on signal statistics and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data if not available. nGpFBMP utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean square error (MMSE) estimate of the sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator.

Data science is a discipline that provides principles, methodology and guidelines for the analysis of data for tools, values, or insights. Driven by a huge workforce demand, many academic institutions have started to offer degrees in data science, with many at the graduate, and a few at the undergraduate level. Curricula may differ at different institutions, because of varying levels of faculty expertise, and different disciplines (such as Math, computer science, and business etc) in developing the curriculum. The University of Massachusetts Dartmouth started offering degree programs in data science from Fall 2015, at both the undergraduate and the graduate level. Quite a few articles have been published that deal with graduate data science courses, much less so dealing with undergraduate ones. Our discussion will focus on undergraduate course structure and function, and specifically, a first course in data science. Our design of this course centers around a concept called the data science life cycle. That is, we view tasks or steps in the practice of data science as forming a process, consisting of states that indicate how it comes into life, how different tasks in data science depend on or interact with others until the birth of a data product or the reach of a conclusion. Naturally, different pieces of the data science life cycle then form individual parts of the course. Details of each piece are filled up by concepts, techniques, or skills that are popular in industry. Consequently, the design of our course is both "principled" and practical. A significant feature of our course philosophy is that, in line with activity theory, the course is based on the use of tools to transform real data in order to answer strongly motivated questions related to the data.