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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.
Functional data analysis (FDA) is a branch of statistics that analyses data providing information about curves, surfaces or anything else varying over a continuum. In its most general form, under an FDA framework, each sample element of functional data is considered to be a random function. The physical continuum over which these functions are defined is often time, but may also be spatial location, wavelength, probability, etc. Intrinsically, functional data are infinite dimensional. The high intrinsic dimensionality of these data brings challenges for theory as well as computation, where these challenges vary with how the functional data were sampled. However, the high or infinite dimensional structure of the data is a rich source of information and there are many interesting challenges for research and data analysis.
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.
The deviance information criterion (DIC) is a hierarchical modeling generalization of the Akaike information criterion (AIC). It is particularly useful in Bayesian model selection problems where the posterior distributions of the models have been obtained by Markov chain Monte Carlo (MCMC) simulation. DIC is an asymptotic approximation as the sample size becomes large, like AIC. It is only valid when the posterior distribution is approximately multivariate normal.
The Journal of the Royal Statistical Society is a peer-reviewed scientific journal of statistics. It comprises three series and is published by Oxford University Press for the Royal Statistical Society.
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.
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.
Richard John Samworth is the Professor of Statistical Science and the Director of the Statistical Laboratory, University of Cambridge, and a Teaching Fellow of St John's College, Cambridge. He was educated at St John's College, Cambridge. His main research interests are in nonparametric and high-dimensional statistics. Particular topics include shape-constrained density estimation and other nonparametric function estimation problems, nonparametric classification, clustering and regression, the bootstrap and high-dimensional variable selection problems.
David Firth is a British statistician. He is Emeritus Professor in the Department of Statistics at the University of Warwick.
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 and Chair of the Department of Biostatistics in the School of Public Health at the University of California, Los Angeles. He served as the 2022 President of the International Society for Bayesian Analysis.
Alan E. 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.
Hui Zou is currently a professor of statistics at the University of Minnesota.
Daniela M. Witten is an American biostatistician. She is a professor and the Dorothy Gilford Endowed Chair of Mathematical Statistics at the University of Washington. Her research investigates the use of machine learning to understand high-dimensional data.
Raquel Prado is a Venezuelan Bayesian statistician. She is a professor of statistics in the Jack Baskin School of Engineering of the University of California, Santa Cruz, and has been elected president of the International Society for Bayesian Analysis for the 2019 term.
John D. Storey is the William R. Harman '63 and Mary-Love Harman Professor in Genomics at Princeton University. His research is focused on statistical inference of high-dimensional data, particularly genomic data. Storey was the founding director of the Princeton University Center for Statistics and Machine Learning.
Peter Lukas Bühlmann is a Swiss mathematician and statistician.
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.
Richard J. Boys was a statistician best known for his contributions to the Bayesian inference, hidden Markov models and stochastic systems.
Angelika van der Linde-Ploumbidis is a statistician. She earned a Ph.D in 1982 or 1983 at the Freie Universität Berlin with the dissertation Zur numerischen Behandlung von Versuchsplanungsproblemen für lineare Regressionsmodelle mit korrelierten Beobachtungen.
Integrated nested Laplace approximations (INLA) is a method for approximate Bayesian inference based on Laplace's method. It is designed for a class of models called latent Gaussian models (LGMs), for which it can be a fast and accurate alternative for Markov chain Monte Carlo methods to compute posterior marginal distributions. Due to its relative speed even with large data sets for certain problems and models, INLA has been a popular inference method in applied statistics, in particular spatial statistics, ecology, and epidemiology. It is also possible to combine INLA with a finite element method solution of a stochastic partial differential equation to study e.g. spatial point processes and species distribution models. The INLA method is implemented in the R-INLA R package.
References
- 1 2 3 4 Curriculum vitae, retrieved 2015-07-05.
- ↑ David Dunson at the Mathematics Genealogy Project
