Jonathan Williams

Williams, J. P., Ommen, D. M., & Hannig, J. (2023). GENERALIZED FIDUCIAL FACTOR: AN ALTERNATIVE TO THE BAYES FACTOR FOR FORENSIC IDENTIFICATION OF SOURCE PROBLEMS. ANNALS OF APPLIED STATISTICS, 17(1), 378–402. https://doi.org/10.1214/22-AOAS1632

Williams, J. P., Xie, Y., & Hannig, J. (2022, September 27). The EAS approach for graphical selection consistency in vector autoregression models. CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE. https://doi.org/10.1002/cjs.11726

Nghiem, S., Williams, J., Afoakwah, C., Huynh, Q., Ng, S.-kay, & Byrnes, J. (2021). Can Administrative Health Data Improve the Gold Standard? Evidence from a Model of the Progression of Myocardial Infarction. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 18(14). https://doi.org/10.3390/ijerph18147385

Williams, J. P. (2021, July 3). Discussion of "A Gibbs Sampler for a Class of Random Convex Polytopes". JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, Vol. 116, pp. 1198–1200. https://doi.org/10.1080/01621459.2021.1946405

Oh, A., Foster, M. L., Williams, J. G., Zheng, C., Ru, H., Lunn, K. F., & Mowat, F. M. (2019). Diagnostic utility of clinical and laboratory test parameters for differentiating between sudden acquired retinal degeneration syndrome and pituitary‐dependent hyperadrenocorticism in dogs. Veterinary Ophthalmology, 22(6), 842–858. https://doi.org/10.1111/vop.12661