Statistical inference is the process of using data analysis to infer properties of an underlying distribution of probability. Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates. It is assumed that the observed data set is sampled from a larger population.
Mathematical statistics is the application of probability theory, a branch of mathematics, to statistics, as opposed to techniques for collecting statistical data. Specific mathematical techniques which are used for this include mathematical analysis, linear algebra, stochastic analysis, differential equations, and measure theory.
Alberto Abadie is professor of the department of economics at MIT and Associate Director of the Institute for Data, Systems, and Society (IDSS) also at MIT. He was born in the Basque Country, Spain. He received his PhD in economics from MIT in 1999. Upon graduating, he joined the faculty at the Harvard Kennedy School, where he was promoted to full professor in 2005. He returned to MIT in 2016.
In probability theory, an empirical process is a stochastic process that describes the proportion of objects in a system in a given state. For a process in a discrete state space a population continuous time Markov chain or Markov population model is a process which counts the number of objects in a given state . In mean field theory, limit theorems are considered and generalise the central limit theorem for empirical measures. Applications of the theory of empirical processes arise in non-parametric statistics.
The American Public Health Association (APHA) is a Washington, D.C.-based professional membership and advocacy organization for public health professionals in the United States. APHA is the largest professional organization of public health professionals in the United States. APHA host the largest gathering of public health professionals in the world at their annual meeting and exhibition.
In statistical hypothesis testing, the null distribution is the probability distribution of the test statistic when the null hypothesis is true. For example, in an F-test, the null distribution is an F-distribution. Null distribution is a tool scientists often use when conducting experiments. The null distribution is the distribution of two sets of data under a null hypothesis. If the results of the two sets of data are not outside the parameters of the expected results, then the null hypothesis is said to be true.
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.
In medical research, a dynamic treatment regime (DTR), adaptive intervention, or adaptive treatment strategy is a set of rules for choosing effective treatments for individual patients. Historically, medical research and the practice of medicine tended to rely on an acute care model for the treatment of all medical problems, including chronic illness. Treatment choices made for a particular patient under a dynamic regime are based on that individual's characteristics and history, with the goal of optimizing his or her long-term clinical outcome. A dynamic treatment regime is analogous to a policy in the field of reinforcement learning, and analogous to a controller in control theory. While most work on dynamic treatment regimes has been done in the context of medicine, the same ideas apply to time-varying policies in other fields, such as education, marketing, and economics.
Murray Rosenblatt was a statistician specializing in time series analysis who was a professor of mathematics at the University of California, San Diego. He received his Ph.D. at Cornell University. He was also a recipient of a Guggenheim Fellowship, in 1965, and was a member of the National Academy of Sciences. He wrote about 140 research articles, 4 books, and co-edited 6 books.
Zhiliang Ying is a Professor of Statistics in the Department of Statistics, Columbia University. He served as co-chair of the department.
Guido Wilhelmus Imbens is a Dutch-American economist whose research concerns econometrics and statistics. He holds the Applied Econometrics Professorship in Economics at the Stanford Graduate School of Business at Stanford University, where he has taught since 2012.
Victor Chernozhukov is a Russian-American statistician and economist currently at Massachusetts Institute of Technology. His current research focuses on mathematical statistics and machine learning for causal structural models in high-dimensional environments. He graduated from the University of Illinois at Urbana-Champaign with a master's in statistics in 1997 and received his PhD in economics from Stanford University in 2000.
In statistics, in particular in the design of experiments, a multi-valued treatment is a treatment that can take on more than two values. It is related to the dose-response model in the medical literature.
Kathryn M. Roeder is an American statistician known for her development of statistical methods to uncover the genetic basis of complex disease and her contributions to mixture models, semiparametric inference, and multiple testing. Roeder holds positions as professor of statistics and professor of computational biology at Carnegie Mellon University, where she leads a project focused on discovering genes associated with autism.
Dorota Maria Dabrowska is a Polish statistician known for applying nonparametric statistics and semiparametric models to counting processes and survival analysis. Dabrowska's estimator, from her paper "Kaplan–Meier estimate on the plane" is a widely used tool for bivariate survival under random censoring.
Dipak Kumar Dey is an Indian-American statistician best known for his work on Bayesian methodologies. He is currently the Board of Trustees Distinguished Professor in the Department of Statistics at the University of Connecticut. Dey has an international reputation as a statistician as well as a data scientist. Since he earned a Ph.D. degree in statistics from Purdue University in 1980, Dey has made tremendous contributions to the development of modern statistics, especially in Bayesian analysis, decision science and model selection. Dey has published more than 10 books and edited volumes, and over 260 research articles in peer-refereed national and international journals. In addition, the statistical methodologies that he has developed has found wide applications in a plethora of interdisciplinary and applied fields, such as biometry and bioinformatics, genetics, econometrics, environmental science, and social science. Dey has supervised 40 Ph.D. students, and presented more than 200 professional talks in colloquia, seminars and conferences all over the world. During his career, Dey has been a visiting professor or scholar at many institutions or research centers around the world, such as Macquarie University, Pontificia Universidad Católica de Chile,, University of São Paulo, University of British Columbia, Statistical and Applied Mathematical Sciences Institute, etc. Dey is an elected fellow of the American Association for the Advancement of Science, the American Statistical Association, the Institute of Mathematical Statistics, the International Society for Bayesian Analysis and the International Statistical Institute.
Xiaohong Chen is a Chinese economist who currently serves as the Malcolm K. Brachman Professor of Economics at Yale University. She is a fellow of the Econometric Society and a laureate of the China Economics Prize. As one of the leading experts in econometrics, her research focuses on econometric theory, Semi/nonparametric estimation and inference methods, Sieve methods, Nonlinear time series, and Semi/nonparametric models. She was elected to the American Academy of Arts and Sciences in 2019.
Sherri Rose is an American biostatistician. She is an associate professor of health care policy at Stanford University, having formally 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.
Marloes Henriette Maathuis is a Dutch statistician known for her work on causal inference using graphical models, particularly in high-dimensional data from applications in biology and epidemiology. She is a professor of statistics at ETH Zurich in Switzerland.
