| Year |
Citation |
Score |
| 2020 |
Schuemie MJ, Ryan PB, Pratt N, Chen R, You SC, Krumholz HM, Madigan D, Hripcsak G, Suchard MA. Principles of Large-scale Evidence Generation and Evaluation across a Network of Databases (LEGEND). Journal of the American Medical Informatics Association : Jamia. 27: 1331-1337. PMID 32909033 DOI: 10.1093/Jamia/Ocaa103 |
0.312 |
|
| 2018 |
Schuemie MJ, Ryan PB, Hripcsak G, Madigan D, Suchard MA. Improving reproducibility by using high-throughput observational studies with empirical calibration. Philosophical Transactions. Series a, Mathematical, Physical, and Engineering Sciences. 376. PMID 30082302 DOI: 10.1098/Rsta.2017.0356 |
0.34 |
|
| 2017 |
Shahn Z, Madigan D. Latent Class Mixture Models of Treatment Effect Heterogeneity Bayesian Analysis. 12: 831-854. DOI: 10.1214/16-Ba1022 |
0.321 |
|
| 2016 |
Shaddox TR, Ryan PB, Schuemie MJ, Madigan D, Suchard MA. Hierarchical Models for Multiple, Rare Outcomes Using Massive Observational Healthcare Databases. Statistical Analysis and Data Mining. 9: 260-268. PMID 28503249 DOI: 10.1002/Sam.11324 |
0.366 |
|
| 2015 |
Letham B, Rudin C, McCormick TH, Madigan D. Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model Annals of Applied Statistics. 9: 1350-1371. DOI: 10.1214/15-Aoas848 |
0.374 |
|
| 2014 |
Mittal S, Madigan D, Burd RS, Suchard MA. High-dimensional, massive sample-size Cox proportional hazards regression for survival analysis. Biostatistics (Oxford, England). 15: 207-21. PMID 24096388 DOI: 10.1093/Biostatistics/Kxt043 |
0.332 |
|
| 2014 |
Schuemie MJ, Ryan PB, DuMouchel W, Suchard MA, Madigan D. Interpreting observational studies: why empirical calibration is needed to correct p-values. Statistics in Medicine. 33: 209-18. PMID 23900808 DOI: 10.1002/Sim.5925 |
0.322 |
|
| 2013 |
Schuemie MJ, Madigan D, Ryan PB. Empirical performance of LGPS and LEOPARD: lessons for developing a risk identification and analysis system. Drug Safety. 36: S133-42. PMID 24166230 DOI: 10.1007/S40264-013-0107-X |
0.311 |
|
| 2013 |
Simpson SE, Madigan D, Zorych I, Schuemie MJ, Ryan PB, Suchard MA. Multiple self-controlled case series for large-scale longitudinal observational databases. Biometrics. 69: 893-902. PMID 24117144 DOI: 10.1111/Biom.12078 |
0.31 |
|
| 2013 |
Madigan D, Ryan PB, Schuemie M, Stang PE, Overhage JM, Hartzema AG, Suchard MA, DuMouchel W, Berlin JA. Evaluating the impact of database heterogeneity on observational study results. American Journal of Epidemiology. 178: 645-51. PMID 23648805 DOI: 10.1093/Aje/Kwt010 |
0.311 |
|
| 2013 |
Mittal S, Madigan D, Cheng JQ, Burd RS. Large-scale parametric survival analysis. Statistics in Medicine. 32: 3955-71. PMID 23625862 DOI: 10.1002/Sim.5817 |
0.362 |
|
| 2013 |
Zorych I, Madigan D, Ryan P, Bate A. Disproportionality methods for pharmacovigilance in longitudinal observational databases. Statistical Methods in Medical Research. 22: 39-56. PMID 21878461 DOI: 10.1177/0962280211403602 |
0.309 |
|
| 2013 |
Rudin C, Letham B, Madigan D. Learning theory analysis for association rules and sequential event prediction Journal of Machine Learning Research. 14: 3441-3492. DOI: 10.7916/D82N50C1 |
0.314 |
|
| 2013 |
Ryan P, Suchard MA, Schuemie M, Madigan D. Learning From Epidemiology: Interpreting Observational Database Studies for the Effects of Medical Products Statistics in Biopharmaceutical Research. 5: 170-179. DOI: 10.1080/19466315.2013.791638 |
0.345 |
|
| 2012 |
Ryan PB, Madigan D, Stang PE, Overhage JM, Racoosin JA, Hartzema AG. Empirical assessment of methods for risk identification in healthcare data: results from the experiments of the Observational Medical Outcomes Partnership. Statistics in Medicine. 31: 4401-15. PMID 23015364 DOI: 10.1002/Sim.5620 |
0.313 |
|
| 2012 |
Harpaz R, DuMouchel W, Shah NH, Madigan D, Ryan P, Friedman C. Novel data-mining methodologies for adverse drug event discovery and analysis. Clinical Pharmacology and Therapeutics. 91: 1010-21. PMID 22549283 DOI: 10.1038/Clpt.2012.50 |
0.301 |
|
| 2012 |
McCormick TH, Raftery AE, Madigan D, Burd RS. Dynamic logistic regression and dynamic model averaging for binary classification. Biometrics. 68: 23-30. PMID 21838812 DOI: 10.1111/J.1541-0420.2011.01645.X |
0.604 |
|
| 2010 |
Caster O, Norén GN, Madigan D, Bate A. Large-scale regression-based pattern discovery: The example of screening the WHO global drug safety database Statistical Analysis and Data Mining. 3: 197-208. DOI: 10.1002/Sam.V3:4 |
0.325 |
|
| 2008 |
Burd RS, Ouyang M, Madigan D. Bayesian logistic injury severity score: a method for predicting mortality using international classification of disease-9 codes. Academic Emergency Medicine : Official Journal of the Society For Academic Emergency Medicine. 15: 466-75. PMID 18439203 DOI: 10.1111/J.1553-2712.2008.00105.X |
0.307 |
|
| 2008 |
Balakrishnan S, Madigan D. Algorithms for sparse linear classifiers in the massive data setting Journal of Machine Learning Research. 9: 313-337. DOI: 10.7916/D8Z0368X |
0.512 |
|
| 2007 |
Rolka H, Burkom H, Cooper GF, Kulldorff M, Madigan D, Wong WK. Issues in applied statistics for public health bioterrorism surveillance using multiple data streams: research needs. Statistics in Medicine. 26: 1834-56. PMID 17221940 DOI: 10.1002/Sim.2793 |
0.329 |
|
| 2007 |
Genkin A, Lewis DD, Madigan D. Large-scale bayesian logistic regression for text categorization Technometrics. 49: 291-304. DOI: 10.1198/004017007000000245 |
0.415 |
|
| 2007 |
Balakrishnan S, Madigan D. Finding predictive runs with LAPS Proceedings - Ieee International Conference On Data Mining, Icdm. 415-420. DOI: 10.1109/ICDM.2007.84 |
0.523 |
|
| 2006 |
Balakrishnan S, Madigan D. A one-pass sequential monte carlo method for bayesian analysis of massive datasets Bayesian Analysis. 1: 345-362. DOI: 10.1214/06-Ba112 |
0.496 |
|
| 2006 |
Balakrishnan S, Madigan D. Decision trees for functional variables Proceedings - Ieee International Conference On Data Mining, Icdm. 798-802. DOI: 10.1109/ICDM.2006.49 |
0.464 |
|
| 2004 |
Efron B, Hastie T, Johnstone I, Tibshirani R, Ishwaran H, Knight K, Loubes JM, Massart P, Madigan D, Ridgeway G, Rosset S, Zhu JI, Stine RA, Turlach BA, Weisberg S. Least angle regression Annals of Statistics. 32: 407-499. DOI: 10.1214/009053604000000067 |
0.409 |
|
| 2003 |
Ridgeway G, Madigan D. A Sequential Monte Carlo Method for Bayesian Analysis of Massive Datasets. Data Mining and Knowledge Discovery. 7: 301-319. PMID 19789656 DOI: 10.1023/A:1024084221803 |
0.694 |
|
| 2003 |
Cohen A, Madigan D, Sackrowitz HB. Effective directed tests for models with ordered categorical data Australian and New Zealand Journal of Statistics. 45: 285-300. DOI: 10.1111/1467-842X.00284 |
0.339 |
|
| 2002 |
Hoeting JA, Raftery AE, Madigan D. Bayesian variable and transformation selection in linear regression Journal of Computational and Graphical Statistics. 11: 485-507. DOI: 10.1198/106186002501 |
0.678 |
|
| 2002 |
Madigan D, Raghavan N, Dumouchel W, Nason M, Posse C, Redgeway G. Likelihood-based data squashing: A modeling approach to instance construction Data Mining and Knowledge Discovery. 6: 173-190. DOI: 10.1023/A:1014095614948 |
0.41 |
|
| 2001 |
Andersson SA, Madigan D, Perlman MD. Alternative Markov properties for chain graphs Scandinavian Journal of Statistics. 28: 33-85. DOI: 10.1111/1467-9469.00224 |
0.33 |
|
| 2000 |
Hoeting JA, Madigan D, Raftery AE, Volinsky CT. Correction to: ``Bayesian model averaging: a tutorial'' [Statist.
Sci. 14 (1999), no. 4, 382--417; MR 2001a:62033] Statistical Science. 15: 193-195. DOI: 10.1214/Ss/1009212814 |
0.699 |
|
| 2000 |
Kanungo T, Haralick RM, Baird HS, Stuezle W, Madigan D. A statistical, nonparametric methodology for document degradation model validation Ieee Transactions On Pattern Analysis and Machine Intelligence. 22: 1209-1223. DOI: 10.1109/34.888707 |
0.422 |
|
| 1999 |
Volinsky CT, Raftery AE, Madigan D, Hoeting JA. David Draper and E. I. George, and a rejoinder by the authors Statistical Science. 14: 382-417. DOI: 10.1214/Ss/1009212519 |
0.756 |
|
| 1999 |
Ridgeway G, Richardson T, Madigan D. Statistics and Computing. 9: 150-152. DOI: 10.1023/A:1008815121360 |
0.701 |
|
| 1999 |
Hoeting JA, Madigan D, Raftery AE, Volinsky CT. Bayesian Model Averaging: A Tutorial Statistical Science. 14: 382-417. |
0.749 |
|
| 1997 |
Volinsky CT, Madigan D, Raftery AE, Kronmal RA. Bayesian Model Averaging in Proportional Hazard Models: Assessing the Risk of a Stroke Journal of the Royal Statistical Society: Series C (Applied Statistics). 46: 433-448. DOI: 10.1111/1467-9876.00082 |
0.501 |
|
| 1997 |
Volinsky CT, Madigan D, Raftery AE, Kronmal RA. Bayesian model averaging in proportional hazard models: Assessing the risk of a stroke Journal of the Royal Statistical Society. Series C: Applied Statistics. 46: 433-448. DOI: 10.1111/1467-9876.00082 |
0.426 |
|
| 1997 |
Madigan D, York JC. Bayesian methods for estimation of the size of a closed population Biometrika. 84: 19-31. DOI: 10.1093/Biomet/84.1.19 |
0.323 |
|
| 1997 |
Raftery AE, Madigan D, Hoeting JA. Bayesian model averaging for linear regression models Journal of the American Statistical Association. 92: 179-191. DOI: 10.1080/01621459.1997.10473615 |
0.711 |
|
| 1997 |
Raftery AE, Madigan D, Hoeting JA. Bayesian Model Averaging for Linear Regression Models Journal of the American Statistical Association. 92: 179-191. DOI: 10.1080/01621459.1997.10473615 |
0.724 |
|
| 1997 |
Andersson SA, Madigan D, Perlman MD, Triggs CM. A graphical characterization of lattice conditional independence models Annals of Mathematics and Artificial Intelligence. 21: 27-50. DOI: 10.1023/A:1018901032102 |
0.394 |
|
| 1997 |
Glymour C, Madigan D, Pregibon D, Smyth P. Statistical themes and lessons for data mining Data Mining and Knowledge Discovery. 1: 11-28. DOI: 10.1023/A:1009773905005 |
0.31 |
|
| 1996 |
Glymour C, Madigan D, Pregibon D, Smyth P. Statistical Inference and Data Mining Communications of the Acm. 39: 35-41. DOI: 10.1145/240455.240466 |
0.311 |
|
| 1996 |
Madigan D, Andersson SA, Perlman MD, Volinsky CT. Bayesian model averaging and model selection for Markov equivalence classes of acyclic digraphs Communications in Statistics - Theory and Methods. 25: 2493-2519. DOI: 10.1080/03610929608831853 |
0.413 |
|
| 1996 |
Hoeting J, Raftery AE, Madigan D. A method for simultaneous variable selection and outlier identification in linear regression Computational Statistics and Data Analysis. 22: 251-270. DOI: 10.1016/0167-9473(95)00053-4 |
0.748 |
|
| 1995 |
York J, Madigan D, Heuch I, Lie RT. Birth Defects Registered by Double Sampling: A Bayesian Approach Incorporating Covariates and Model Uncertainty Applied Statistics. 44: 227. DOI: 10.2307/2986347 |
0.331 |
|
| 1995 |
Madigan D, Gavrin J, Raftery AE. Eliciting prior information to enhance the predictive performance of bayesian graphical models Communications in Statistics - Theory and Methods. 24: 2271-2292. DOI: 10.1080/03610929508831616 |
0.574 |
|
| 1995 |
Andersson SA, Madigan D, Perlman MD, Triggs CM. On the relation between conditional independence models determined by finite distributive lattices and by directed acyclic graphs Journal of Statistical Planning and Inference. 48: 25-46. DOI: 10.1016/0378-3758(94)00150-T |
0.348 |
|
| 1994 |
Madigan D, Raftery AE. Model selection and accounting for model uncertainty in graphical models using occam’s window Journal of the American Statistical Association. 89: 1535-1546. DOI: 10.1080/01621459.1994.10476894 |
0.633 |
|
| 1994 |
Madigan D. Graphical models in applied multivariate statistics, by J. Whittaker, John Wiley & Sons, New York, 1990, 448 pp. Price: $59.95 Networks. 24: 125-125. DOI: 10.1002/Net.3230240213 |
0.34 |
|
| 1990 |
Madigan D, Mosurski K. An extension of the results of asmussen and edwards on collapsibility in contingency tables Biometrika. 77: 315-319. DOI: 10.1093/Biomet/77.2.315 |
0.344 |
|
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