H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Mathematics D-index 45 Citations 33,938 91 World Ranking 737 National Ranking 367
Engineering and Technology D-index 51 Citations 38,906 131 World Ranking 1361 National Ranking 575

Research.com Recognitions

Awards & Achievements

2012 - Fellow of the American Association for the Advancement of Science (AAAS)

1999 - Fellow of the American Statistical Association (ASA)

Overview

What is he best known for?

The fields of study he is best known for:

  • Statistics
  • Artificial intelligence
  • Machine learning

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 most cited work include:

  • Least angle regression (6995 citations)
  • Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors (3392 citations)
  • Bayesian Model Averaging for Linear Regression Models (1367 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Artificial intelligence (26.36%)
  • Machine learning (23.64%)
  • Observational study (25.91%)

What were the highlights of his more recent work (between 2014-2021)?

  • Observational study (25.91%)
  • Bayesian probability (18.18%)
  • Internal medicine (5.00%)

In recent papers he was focusing on the following fields of study:

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.

Between 2014 and 2021, his most popular works were:

  • Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model (350 citations)
  • Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers (323 citations)
  • Good practices for real‐world data studies of treatment and/or comparative effectiveness: Recommendations from the joint ISPOR‐ISPE Special Task Force on real‐world evidence in health care decision making (174 citations)

In his most recent research, the most cited papers focused on:

  • Statistics
  • Artificial intelligence
  • Machine learning

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Least angle regression

Bradley Efron;Trevor Hastie;Iain Johnstone;Robert Tibshirani.
Annals of Statistics (2004)

9815 Citations

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)

5279 Citations

Bayesian Model Averaging for Linear Regression Models

Adrian E. Raftery;David Madigan;Jennifer A. Hoeting.
Journal of the American Statistical Association (1997)

1884 Citations

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)

1505 Citations

Bayesian Graphical Models for Discrete Data

David Madigan;Jeremy York.
International Statistical Review (1995)

1329 Citations

Large-Scale Bayesian Logistic Regression for Text Categorization

Alexander Genkin;David D Lewis;David Madigan.
Technometrics (2007)

869 Citations

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)

529 Citations

Bayesian indoor positioning systems

D. Madigan;E. Einahrawy;R.P. Martin;W.-H. Ju.
international conference on computer communications (2005)

527 Citations

A characterization of Markov equivalence classes for acyclic digraphs

Steen A. Andersson;David B. Madigan;Michael D. Perlman.
Annals of Statistics (1997)

503 Citations

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)

478 Citations

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Best Scientists Citing David Madigan

George Hripcsak

George Hripcsak

Columbia University

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Bruno Sudret

Bruno Sudret

ETH Zurich

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Adrian E. Raftery

Adrian E. Raftery

University of Washington

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Francis Bach

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French Institute for Research in Computer Science and Automation - INRIA

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Peter Bühlmann

Peter Bühlmann

ETH Zurich

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Robert Tibshirani

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Stanford University

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Bin Yu

University of California, Berkeley

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Trevor Hastie

Trevor Hastie

Stanford University

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Hui Zou

Hui Zou

University of Minnesota

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Nigam H. Shah

Nigam H. Shah

Stanford University

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David B. Dunson

David B. Dunson

Duke University

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Cynthia Rudin

Cynthia Rudin

Duke University

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Amaury Lendasse

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University of Houston

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