Geostatistics is a branch of statistics focusing on spatial or spatiotemporal datasets. Developed originally to predict probability distributions of ore grades for mining operations, it is currently applied in diverse disciplines including petroleum geology, hydrogeology, hydrology, meteorology, oceanography, geochemistry, geometallurgy, geography, forestry, environmental control, landscape ecology, soil science, and agriculture. Geostatistics is applied in varied branches of geography, particularly those involving the spread of diseases (epidemiology), the practice of commerce and military planning (logistics), and the development of efficient spatial networks. Geostatistical algorithms are incorporated in many places, including geographic information systems (GIS).
In statistics, Markov chain Monte Carlo (MCMC) methods comprise a class of algorithms for sampling from a probability distribution. By constructing a Markov chain that has the desired distribution as its equilibrium distribution, one can obtain a sample of the desired distribution by recording states from the chain. The more steps that are included, the more closely the distribution of the sample matches the actual desired distribution. Various algorithms exist for constructing chains, including the Metropolis–Hastings algorithm.
Sir David John Spiegelhalter is a British statistician and a Fellow of Churchill College, Cambridge. From 2007 to 2018 he was Winton Professor of the Public Understanding of Risk in the Statistical Laboratory at the University of Cambridge. Spiegelhalter is an ISI highly cited researcher.
Spatial analysis is any of the formal techniques which studies entities using their topological, geometric, or geographic properties. Spatial analysis includes a variety of techniques using different analytic approaches, especially spatial statistics. It may be applied in fields as diverse as astronomy, with its studies of the placement of galaxies in the cosmos, or to chip fabrication engineering, with its use of "place and route" algorithms to build complex wiring structures. In a more restricted sense, spatial analysis is geospatial analysis, the technique applied to structures at the human scale, most notably in the analysis of geographic data. It may also be applied to genomics, as in transcriptomics data.
Spatial epidemiology is a subfield of epidemiology focused on the study of the spatial distribution of health outcomes; it is closely related to health geography.
In statistics, Wombling is any of a number of techniques used for identifying zones of rapid change, typically in some quantity as it varies across some geographical or Euclidean space. It is named for statistician William H. Womble.
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.
Ecological forecasting uses knowledge of physics, ecology and physiology to predict how ecological populations, communities, or ecosystems will change in the future in response to environmental factors such as climate change. The goal of the approach is to provide natural resource managers with information to anticipate and respond to short and long-term climate conditions.
Bani K. Mallick is a Distinguished Professor and Susan M. Arseven `75 Chair in Data Science and Computational Statistics in the Department of Statistics at Texas A&M University in College Station. He is the Director of the Center for Statistical Bioinformatics. Mallick is well known for his contribution to the theory and practice of Bayesian semiparametric methods and uncertainty quantification. Mallick is an elected fellow of American Association for the Advancement of Science, American Statistical Association, Institute of Mathematical Statistics, International Statistical Institute and the Royal Statistical Society. He received the Distinguished research award from Texas A&M University and the Young Researcher award from the International Indian Statistical Association.
David Brian Dunson is an American statistician who is Arts and Sciences Distinguished Professor of Statistical Science, Mathematics and Electrical & Computer Engineering at Duke University. His research focuses on developing statistical methods for complex and high-dimensional data. Particular themes of his work include the use of Bayesian hierarchical models, methods for learning latent structure in complex data, and the development of computationally efficient algorithms for uncertainty quantification. He is currently serving as joint Editor of the Journal of the Royal Statistical Society, Series B.
Vanja Dukic is an expert in computational statistics and mathematical epidemiology who works as a professor of applied mathematics at the University of Colorado Boulder. Her research includes work on using internet search engine access patterns to track diseases, and on the effects of climate change on the spread of diseases.
Alan Enoch Gelfand is an American statistician, and is currently the James B. Duke Professor of Statistics and Decision Sciences at Duke University. Gelfand’s research includes substantial contributions to the fields of Bayesian statistics, spatial statistics and hierarchical modeling.
Amy Helen Herring is an American biostatistician interested in longitudinal data and reproductive health. Formerly the Carol Remmer Angle Distinguished Professor of Children's Environmental Health at the University of North Carolina at Chapel Hill, she is now Sara & Charles Ayres Distinguished Professor in the Department of Statistical Science, Global Health Institute, and Department of Biostatistics & Bioinformatics of Duke University.
Elizabeth Anne (Lianne) Sheppard is an American statistician. She specializes in biostatistics and environmental statistics, and in particular in the effects of air quality on health. She is a Professor of Environmental and Occupational Health Sciences and a Professor of Biostatistics in the University of Washington School of Public Health. In 2021, Dr. Sheppard was named to the Rohm & Haas Endowed Professorship of Public Health Sciences.
Dalene Kay Stangl is an American statistician known for development and promotion of Bayesian statistical methods in health-related research.
Robert E. Kass is the Maurice Falk Professor of Statistics and Computational Neuroscience in the Department of Statistics and Data Science, the Machine Learning Department, and the Neuroscience Institute at Carnegie Mellon University.
Vecchia approximation is a Gaussian processes approximation technique originally developed by Aldo Vecchia, a statistician at United States Geological Survey. It is one of the earliest attempts to use Gaussian processes in high-dimensional settings. It has since been extensively generalized giving rise to many contemporary approximations.
Roderick Joseph Alexander Little is an academic statistician, whose main research contributions lie in the statistical analysis of data with missing values and the analysis of complex sample survey data. Little is Richard D. Remington Distinguished University Professor of Biostatistics in the Department of Biostatistics at the University of Michigan, where he also holds academic appointments in the Department of Statistics and the Institute for Social Research.
Barbara Elizabeth Engelhardt is an American computer scientist and specialist in bioinformatics. Working as a Professor at Stanford University, her work has focused on latent variable models, exploratory data analysis for genomic data, and QTLs. In 2021, she was awarded the Overton Prize by the International Society for Computational Biology.
Guosheng Yin is a statistician, data scientist, educator and researcher in Biostatistics, Statistics, machine learning, and AI. Presently, Guosheng Yin is Chair in Statistics in Department of Mathematics at Imperial College London. Previously, he served as the Head of Department and the Patrick S C Poon Endowed Chair in Statistics and Actuarial Science, at the University of Hong Kong. Before he joined the University of Hong Kong, Yin worked at the University of Texas M.D. Anderson Cancer Center till 2009 as a tenured Associate Professor of Biostatistics.
