The Work of Raymond J. Carroll ─ The Impact and Influence of a Statistician

This volume contains Raymond J. Carroll's research and commentary on its impact by leading statisticians. Each of the seven main parts focuses on a key research area: Measurement Error, Transformation and Weighting, Epidemiology, Nonparametric and Semiparametric Regression for Independent Data, Nonparametric and Semiparametric Regression for Dependent Data, Robustness, and other work. The seven subject areas reviewed in this book were chosen by Ray himself, as were the articles representing each area. The commentaries not only review Ray’s work, but are also filled with history and anecdotes. Raymond J. Carroll’s impact on statistics and numerous other fields of science is far-reaching. His vast catalog of work spans from fundamental contributions to statistical theory to innovative methodological development and new insights in disciplinary science. From the outset of his career, rather than taking the “safe” route of pursuing incremental advances, Ray has focused on tackling the most important challenges. In doing so, it is fair to say that he has defined a host of statistics areas, including weighting and transformation in regression, measurement error modeling, quantitative methods for nutritional epidemiology and non- and semiparametric regression.

Raymond J. Carroll is Distinguished Professor of Statistics, Nutrition, and Toxicology at Texas A&M University, where he has been on the faculty since 1987. He was the first statistician ever given a Method to Extend Research In Time (MERIT) Award from the National Cancer Institute (NCI) of the National Institutes of Health (NIH), receiving this honor for his seminal contributions to statistical methodology and the impact of that methodology on public health. He is the principal investigator of a NCI-funded Bioinformatics training program, and is the founding director of the Texas A&M Center for Statistical Bioinformatics. He is also the Director for the Texas A&M Institute of Applied Mathematics and Computational Science.