| The Bayesian information criterion: background, derivation, and applications AA Neath, JE Cavanaugh Wiley Interdisciplinary Reviews: Computational Statistics 4 (2), 199-203, 2012 | 833 | 2012 |
| The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements JE Cavanaugh, AA Neath Wiley Interdisciplinary Reviews: Computational Statistics 11 (3), e1460, 2019 | 621 | 2019 |
| Generalizing the derivation of the Schwarz information criterion JE Cavanaugh, AA Neath Communications in Statistics-Theory and Methods 28 (1), 49-66, 1999 | 141 | 1999 |
| Regression and time series model selection using variants of the Schwarz information criterion AA Neath, JE Cavanaugh Communications in Statistics-Theory and Methods 26 (3), 559-580, 1997 | 104 | 1997 |
| On the efficacy of Bayesian inference for nonidentifiable models AA Neath, FJ Samaniego The American Statistician 51 (3), 225-232, 1997 | 89 | 1997 |
| Estimation optimality of corrected AIC and modified Cp in linear regression SL Davies, AA Neath, JE Cavanaugh International statistical review 74 (2), 161-168, 2006 | 44 | 2006 |
| Symptom clusters in women with relapsing-remitting multiple sclerosis PK Newland, A Fearing, M Riley, A Neath Journal of Neuroscience Nursing 44 (2), 66-71, 2012 | 34 | 2012 |
| Akaike˘s information criterion: Background, derivation, properties, and refinements JE Cavanaugh, AA Neath International encyclopedia of statistical science, 26-29, 2011 | 27 | 2011 |
| A Bayesian approach to the multiple comparisons problem AA Neath, JE Cavanaugh Journal of Data Science 4 (2), 131-146, 2006 | 27 | 2006 |
| Cross validation model selection criteria for linear regression based on the Kullback–Leibler discrepancy SL Davies, AA Neath, JE Cavanaugh Statistical Methodology 2 (4), 249-266, 2005 | 27 | 2005 |
| Polya tree distributions for statistical modeling of censored data AA Neath Journal of Applied Mathematics and Decision Sciences 7 (3), 175-186, 2003 | 18 | 2003 |
| On Bayesian estimation of the multiple decrement function in the competing risks problem AA Neath, FJ Samaniego Statistics & probability letters 31 (2), 75-83, 1996 | 18 | 1996 |
| How to be a better Bayesian FJ Samaniego, AA Neath Journal of the American Statistical Association 91 (434), 733-742, 1996 | 17 | 1996 |
| Performance of variable selection methods in regression using variations of the Bayesian information criterion T Burr, H Fry, B McVey, E Sander, J Cavanaugh, A Neath Communications in Statistics—Simulation and Computation® 37 (3), 507-520, 2008 | 13 | 2008 |
| On the total time on test transform of an IFRA distribution AA Neath, FJ Samaniego Statistics & probability letters 14 (4), 289-291, 1992 | 12 | 1992 |
| Bayesian multiple comparisons and model selection AA Neath, JE Flores, JE Cavanaugh Wiley Interdisciplinary Reviews: Computational Statistics 10 (2), e1420, 2018 | 11 | 2018 |
| A regression model selection criterion based on bootstrap bumping for use with resistant fitting AA Neath, JE Cavanaugh Computational statistics & data analysis 35 (2), 155-169, 2000 | 8 | 2000 |
| Model evaluation, discrepancy function estimation, and social choice theory AA Neath, JE Cavanaugh, AG Weyhaupt Computational statistics 30, 231-249, 2015 | 7 | 2015 |
| Wiley Interdiscip. Rev AA Neath, JE Cavanaugh Comput. Stat 4, 199, 2012 | 7 | 2012 |
| A note on the comparison of the bayesian and frequentist approaches to estimation AA Neath, N Langenfeld Advances in Decision Sciences 2012, 2012 | 6 | 2012 |
Joseph E. CavanaughProfessor of Biostatistics, The University of IowaVerified email at uiowa.edu
Joseph E. CavanaughProfessor of Biostatistics, The University of IowaVerified email at uiowa.edu