User profiles for "author:David Madigan"
David MadiganProfessor 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
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 …
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
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 …
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 …
that are novel by virtue of their clinical nature, severity and/or frequency. The cornerstone of …
Bayesian model averaging for linear regression models
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 …
Conditioning on a single selected model ignores model uncertainty, and thus leads to the …
Large-scale Bayesian logistic regression for text categorization
Logistic regression analysis of high-dimensional data, such as natural language text, poses
computational and statistical challenges. Maximum likelihood estimation often fails in these …
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 …
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 …
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
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 …
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
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 …
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 …
graphs, are widely used to describe dependencies among variables in multiviarate …