Adam J. Branscum, Ph.D.
Affiliations: | 2005 | University of California, Davis, Davis, CA |
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"Adam Branscum"Parents
Sign in to add mentor| Wesley Johnson | grad student | 2005 | UC Davis | |
| (Epidemiologic modeling and data analysis: Bayesian parametric, nonparametric, and semiparametric approaches.) | ||||
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Publications
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| de la Cruz ML, Branscum AJ, Nacar J, et al. (2018) Evaluation of the Performance of the IDvet IFN-Gamma Test for Diagnosis of Bovine Tuberculosis in Spain. Frontiers in Veterinary Science. 5: 229 |
| Carvalho VI, Branscum AJ. (2017) Bayesian nonparametric inference for the three-class Youden index and its associated optimal cutoff points. Statistical Methods in Medical Research. 962280217742538 |
| Kostoulas P, Nielsen SS, Branscum AJ, et al. (2017) Reporting guidelines for diagnostic accuracy studies that use Bayesian latent class models (STARD-BLCM). Statistics in Medicine |
| Inácio de Carvalho V, de Carvalho M, Branscum AJ. (2017) Nonparametric Bayesian covariate-adjusted estimation of the Youden index. Biometrics |
| Kostoulas P, Nielsen SS, Branscum AJ, et al. (2017) STARD-BLCM: Standards for the Reporting of Diagnostic accuracy studies that use Bayesian Latent Class Models. Preventive Veterinary Medicine. 138: 37-47 |
| Branscum AJ, Johnson WO, Hanson TE, et al. (2015) Flexible regression models for ROC and risk analysis, with or without a gold standard. Statistics in Medicine |
| Branscum AJ, Cheng D, Lee JJ. (2015) Testing hypotheses about medical test accuracy: considerations for design and inference Journal of Applied Statistics. 42: 1106-1119 |
| Liu W, Zhang B, Zhang Z, et al. (2015) Model selection in finite mixture of regression models: a Bayesian approach with innovative weighted g priors and reversible jump Markov chain Monte Carlo implementation Journal of Statistical Computation and Simulation. 85: 2456-2478 |
| Hanson TE, Branscum AJ, Johnson WO. (2014) Informative g-priors for logistic regression Bayesian Analysis. 9: 597-612 |
| Schörgendorfer A, Branscum AJ. (2013) Regression analysis using dependent Polya trees. Statistics in Medicine. 32: 4679-95 |