User profiles for "author:David Madigan"

David Madigan

Professor of Statistics, Northeastern University
Verified email at northeastern.edu
Cited by 28430

Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and EI George, and a rejoinder by the authors

JA Hoeting, D Madigan, AE Raftery… - Statistical …, 1999 - projecteuclid.org
Standard statistical practice ignores model uncertainty. Data analysts typically select a
model from some class of models and then proceed as if the selected model had generated …

Novel data‐mining methodologies for adverse drug event discovery and analysis

R Harpaz, W DuMouchel, NH Shah… - Clinical …, 2012 - Wiley Online Library
An important goal of the health system is to identify new adverse drug events (ADEs) in the
postapproval period. Data‐mining methods that can transform data into meaningful …

The role of data mining in pharmacovigilance

M Hauben, D Madigan, CM Gerrits… - Expert opinion on …, 2005 - Taylor & Francis
A principle concern of pharmacovigilance is the timely detection of adverse drug reactions
that are novel by virtue of their clinical nature, severity and/or frequency. The cornerstone of …

Bayesian model averaging for linear regression models

AE Raftery, D Madigan, JA Hoeting - Journal of the American …, 1997 - Taylor & Francis
We consider the problem of accounting for model uncertainty in linear regression models.
Conditioning on a single selected model ignores model uncertainty, and thus leads to the …

Large-scale Bayesian logistic regression for text categorization

A Genkin, DD Lewis, D Madigan - technometrics, 2007 - Taylor & Francis
Logistic regression analysis of high-dimensional data, such as natural language text, poses
computational and statistical challenges. Maximum likelihood estimation often fails in these …

Model selection and accounting for model uncertainty in graphical models using Occam's window

D Madigan, AE Raftery - Journal of the American Statistical …, 1994 - Taylor & Francis
We consider the problem of model selection and accounting for model uncertainty in high-
dimensional contingency tables, motivated by expert system applications. The approach …

Bayesian graphical models for discrete data

D Madigan, J York, D Allard - International Statistical Review/Revue …, 1995 - JSTOR
For more than half a century, data analysts have used graphs to represent statistical models.
In particular, graphical" conditional independence" models have emerged as a useful class …

[HTML][HTML] Observational Health Data Sciences and Informatics (OHDSI): opportunities for observational researchers

G Hripcsak, JD Duke, NH Shah, CG Reich… - Studies in health …, 2015 - ncbi.nlm.nih.gov
The vision of creating accessible, reliable clinical evidence by accessing the clinical
experience of hundreds of millions of patients across the globe is a reality. The …

Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model

B Letham, C Rudin, TH McCormick, D Madigan - 2015 - projecteuclid.org
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction
model Page 1 The Annals of Applied Statistics 2015, Vol. 9, No. 3, 1350–1371 DOI …

A characterization of Markov equivalence classes for acyclic digraphs

SA Andersson, D Madigan, MD Perlman - The Annals of Statistics, 1997 - projecteuclid.org
Undirected graphs and acyclic digraphs (ADG's), as well as their mutual extension to chain
graphs, are widely used to describe dependencies among variables in multiviarate …