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Trygve Magnus Haavelmo, born in Skedsmo, Norway, was an economist whose research interests centered on econometrics. He received the Nobel Memorial Prize in Economic Sciences in 1989.
Lilliefors test is a normality test based on the Kolmogorov–Smirnov test. It is used to test the null hypothesis that data come from a normally distributed population, when the null hypothesis does not specify which normal distribution; i.e., it does not specify the expected value and variance of the distribution. It is named after Hubert Lilliefors, professor of statistics at George Washington University.
In causal inference, a confounder is a variable that influences both the dependent variable and independent variable, causing a spurious association. Confounding is a causal concept, and as such, cannot be described in terms of correlations or associations. The existence of confounders is an important quantitative explanation why correlation does not imply causation. Some notations are explicitly designed to identify the existence, possible existence, or non-existence of confounders in causal relationships between elements of a system.
Sander Greenland is an American statistician and epidemiologist with many contributions to statistical and epidemiologic methods including Bayesian and causal inference, bias analysis, and meta-analysis. His focus has been the extensions, limitations, and misuses of statistical methods in nonexperimental studies, especially in postmarketing surveillance of drugs, vaccines, and medical devices. He received honors Bachelor's and master's degrees in mathematics from the University of California, Berkeley, where he was Regent's and National Science Foundation Fellow in Mathematics, and then received Master's and Doctoral degrees in epidemiology from the University of California, Los Angeles (UCLA), where he was Regent's Fellow in Epidemiology. After serving as an assistant professor of biostatistics at Harvard, he joined the UCLA Epidemiology faculty in 1980 where he became Professor of Epidemiology in the Fielding School of Public Health in 1989, and Professor of Statistics in the UCLA College of Letters and Science in 1999. He moved to Emeritus status in 2012 and the following year he was awarded an honorary Doctor of Medicine by the University of Aarhus, Denmark.
James M. Robins is an epidemiologist and biostatistician best known for advancing methods for drawing causal inferences from complex observational studies and randomized trials, particularly those in which the treatment varies with time. He is the 2013 recipient of the Nathan Mantel Award for lifetime achievement in statistics and epidemiology, and a recipient of the 2022 Rousseeuw Prize in Statistics, jointly with Miguel Hernán, Eric Tchetgen-Tchetgen, Andrea Rotnitzky and Thomas Richardson.
Matching is a statistical technique that evaluates the effect of a treatment by comparing the treated and the non-treated units in an observational study or quasi-experiment. The goal of matching is to reduce bias for the estimated treatment effect in an observational-data study, by finding, for every treated unit, one non-treated unit(s) with similar observable characteristics against which the covariates are balanced out. By matching treated units to similar non-treated units, matching enables a comparison of outcomes among treated and non-treated units to estimate the effect of the treatment reducing bias due to confounding. Propensity score matching, an early matching technique, was developed as part of the Rubin causal model, but has been shown to increase model dependence, bias, inefficiency, and power and is no longer recommended compared to other matching methods. A simple, easy-to-understand, and statistically powerful method of matching known as Coarsened Exact Matching or CEM.
In statistics and causal graphs, a variable is a collider when it is causally influenced by two or more variables. The name "collider" reflects the fact that in graphical models, the arrow heads from variables that lead into the collider appear to "collide" on the node that is the collider. They are sometimes also referred to as inverted forks.
Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable when a cause of the effect variable is changed. The study of why things occur is called etiology, and can be described using the language of scientific causal notation. Causal inference is said to provide the evidence of causality theorized by causal reasoning.
Marginal structural models are a class of statistical models used for causal inference in epidemiology. Such models handle the issue of time-dependent confounding in evaluation of the efficacy of interventions by inverse probability weighting for receipt of treatment, they allow us to estimate the average causal effects. For instance, in the study of the effect of zidovudine in AIDS-related mortality, CD4 lymphocyte is used both for treatment indication, is influenced by treatment, and affects survival. Time-dependent confounders are typically highly prognostic of health outcomes and applied in dosing or indication for certain therapies, such as body weight or lab values such as alanine aminotransferase or bilirubin.
Mary Elizabeth (Betz) Halloran is an American biostatistician who works as a professor of biostatistics, professor of epidemiology, and adjunct professor of applied mathematics at the University of Washington.
Sudipto Banerjee is an Indian-American statistician best known for his work on Bayesian hierarchical modeling and inference for spatial data analysis. He is Professor of Biostatistics and Senior Associate Dean in the School of Public Health at the University of California, Los Angeles. He served as the Chair of the Department of Biostatistics at UCLA from 2014 through 2023. He served as the elected President of the International Society for Bayesian Analysis in 2022.
Paul R. Rosenbaum is the Robert G. Putzel Professor Emeritus in the Department of Statistics and Data Science at Wharton School of the University of Pennsylvania, where he worked from 1986 through 2021. He has written extensively about causal inference in observational studies, including sensitivity analysis, optimal matching, design sensitivity, evidence factors, quasi-experimental devices, and the propensity score. With various coauthors, he has also written about health outcomes, racial disparities in health outcomes, instrumental variables, psychometrics and experimental design.
Miguel Hernán is a Spanish–American epidemiologist. He is the Director of the CAUSALab, Kolokotrones Professor of Biostatistics and Epidemiology at the Harvard T.H. Chan School of Public Health, and Member of the Faculty at the Harvard–MIT Program in Health Sciences and Technology.
Tyler J. VanderWeele is the John L. Loeb and Frances Lehman Loeb Professor of Epidemiology in the Departments of Epidemiology and Biostatistics at the Harvard T.H. Chan School of Public Health. He is also the co-director of Harvard University's Initiative on Health, Religion and Spirituality, the director of their Human Flourishing Program, and a faculty affiliate of the Harvard Institute for Quantitative Social Science. He holds degrees from the University of Oxford, University of Pennsylvania, and Harvard University in mathematics, philosophy, theology, finance, and biostatistics.
Eleanor (Ellie) Jane Murray is a British-Canadian epidemiologist, science communicator, and assistant professor at the Boston University School of Public Health. Throughout the COVID-19 pandemic, Murray created a series of multi-lingual, accessible infographics to communicate information about COVID-19.
Sherri Rose is an American biostatistician. She is an associate professor of health care policy at Stanford University, and once worked at Harvard University. A fellow of the American Statistical Association, she has served as co-editor of Biostatistics since 2019 and Chair of the American Statistical Association’s Biometrics Section. Her research focuses on statistical machine learning for health care policy.
Donna Spiegelman is a biostatistician and epidemiologist who works at the interface between the two fields as a methodologist, applying statistical solutions to address potential biases in epidemiologic studies.
Michael David Escobar is an American biostatistician who is known for Bayesian nonparametrics, mixture models.
John Anthony Hartigan is an Australian-American statistician, the Eugene Higgins Professor of Statistics emeritus at Yale University. He made fundamental contributions to clustering algorithms, including the famous Hartigan-Wong method and biclustering, and Bayesian statistics.
David S. Stoffer is an American statistician, and Professor Emeritus of Statistics at the University of Pittsburgh. He is the author of several books on time series analysis, Time Series Analysis and Its Applications: With R Examples with R.H. Shumway, Nonlinear Time Series: Theory, Methods, and Applications with R Examples with R. Douc and E. Moulines, and Time Series: A Data Analysis Approach Using R with R.H. Shumway.
References
- ↑ "Brumback, Babette", Faculty, University of Florida Department of Biostatistics, retrieved 2020-06-03
- 1 2 3 Curriculum vitae (PDF), retrieved 2020-06-03
- ↑ Babette Brumback at the Mathematics Genealogy Project
- 1 2 Florida: Faculty Named Fellow of American Statistical Association, Association of Schools and Programs of Public Health, May 10, 2019, archived from the original on 2021-01-23, retrieved 2020-06-04
