2012 - Fellow of the American Association for the Advancement of Science (AAAS)
1999 - Fellow of the American Statistical Association (ASA)
Observational study, Statistics, Bayesian inference, Econometrics and Artificial intelligence are his primary areas of study. The study incorporates disciplines such as Research design, Health informatics, Study heterogeneity, Informatics and Cohort in addition to Observational study. David Madigan focuses mostly in the field of Statistics, narrowing it down to topics relating to Feature selection and, in certain cases, Posterior probability, Regression analysis, Outlier and Logistic model tree.
His Bayesian inference study integrates concerns from other disciplines, such as Graphical model, Linear model and Markov chain Monte Carlo. The various areas that David Madigan examines in his Markov chain Monte Carlo study include Linear regression, Focused information criterion, Model selection, Bayesian statistics and Markov chain. His Artificial intelligence research includes elements of Machine learning and Logistic regression.
His scientific interests lie mostly in Artificial intelligence, Machine learning, Observational study, Bayesian probability and Data mining. His work in the fields of Association rule learning overlaps with other areas such as Context and Supervised learning. To a larger extent, David Madigan studies Statistics with the aim of understanding Observational study.
His Statistics course of study focuses on Econometrics and Multivariate statistics. Within one scientific family, David Madigan focuses on topics pertaining to Graphical model under Bayesian probability, and may sometimes address concerns connected to Variable-order Bayesian network. Markov chain is closely connected to Markov chain Monte Carlo in his research, which is encompassed under the umbrella topic of Bayesian inference.
David Madigan mainly focuses on Observational study, Bayesian probability, Internal medicine, Econometrics and Machine learning. His Econometrics research is multidisciplinary, incorporating elements of Meta-analysis, Linear regression and Time series. His study in Machine learning is interdisciplinary in nature, drawing from both Consistency, Null and Artificial intelligence.
David Madigan works mostly in the field of Data science, limiting it down to topics relating to Regression analysis and, in certain cases, Database, as a part of the same area of interest. Confidence interval is a subfield of Statistics that he investigates. His work carried out in the field of Statistics brings together such families of science as Statistical classification and Categorization.
His main research concerns Observational study, Health policy, Informatics, Health informatics and Comparative effectiveness research. David Madigan combines subjects such as Calibration, Sample size determination and Confounding with his study of Observational study. His Sample size determination study deals with the bigger picture of Statistics.
His Informatics research is multidisciplinary, relying on both Health services research, Health Administration Informatics, Observational Studies as Topic and Data science. His Health informatics research incorporates themes from Clinical trial, Gerontology, Econometrics, Generalizability theory and Cohort. His biological study spans a wide range of topics, including Health technology, Evidence-based medicine, Medical education and Outcomes research.
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Least angle regression
Bradley Efron;Trevor Hastie;Iain Johnstone;Robert Tibshirani.
Annals of Statistics (2004)
Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors
Jennifer A. Hoeting;David Madigan;Adrian E. Raftery;Chris T. Volinsky.
Statistical Science (1999)
Bayesian Model Averaging for Linear Regression Models
Adrian E. Raftery;David Madigan;Jennifer A. Hoeting.
Journal of the American Statistical Association (1997)
Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window
David Madigan;Adrian E. Raftery.
Journal of the American Statistical Association (1994)
Bayesian Graphical Models for Discrete Data
David Madigan;Jeremy York.
International Statistical Review (1995)
Large-Scale Bayesian Logistic Regression for Text Categorization
Alexander Genkin;David D Lewis;David Madigan.
Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers
George Hripcsak;Jon D. Duke;Nigam H. Shah;Christian G. Reich.
Studies in health technology and informatics (2015)
Bayesian indoor positioning systems
D. Madigan;E. Einahrawy;R.P. Martin;W.-H. Ju.
international conference on computer communications (2005)
A characterization of Markov equivalence classes for acyclic digraphs
Steen A. Andersson;David B. Madigan;Michael D. Perlman.
Annals of Statistics (1997)
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
Benjamin Letham;Cynthia Rudin;Tyler H. McCormick;David Madigan.
The Annals of Applied Statistics (2015)
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