Jonathan Williams

Multivariate and Online Transfer Learning With Uncertainty Quantification

Hickey, J., Williams, J. P., Reich, B. J., & Hector, E. C. (2026, February 1). Statistics in Medicine.

Bin-Conditional Conformal Prediction of Fatalities from Armed Conflict

Randahl, D., Williams, J. P., & Hegre, H. (2025, July 30). Political Analysis.

Conformal prediction for astronomy data with measurement error

Singer, N., Williams, J. P., & Ghosh, S. (2025, April 1). Monthly Notices of the Royal Astronomical Society, Vol. 539, pp. 1372–1380.

Generalized universal inference on risk minimizers

Dey, N., Martin, R., & Williams, J. P. (2025, October 13). Journal of the Royal Statistical Society Series B (Statistical Methodology).

Multiple testing in generalized universal inference

Dey, N., Martin, R., & Williams, J. P. (2025, September 22). Statistics & Probability Letters.

Uncertainty quantification in automated valuation models with spatially weighted conformal prediction

Hjort, A., Hermansen, G. H., Pensar, J., & Williams, J. P. (2025, July 29). International Journal of Data Science and Analytics.

A statistical primer on classical period-finding techniques in astronomy

Giertych, N., Shaban, A., Haravu, P., & Williams, J. P. (2024, June 20). Reports on Progress in Physics, Vol. 87.

Bayesian hidden Markov models for latent variable labeling assignments in conflict research: Application to the role ceasefires play in conflict dynamics

Williams, J. P., Hermansen, G. H., Strand, H., Clayton, G., & Nygård, H. M. (2024, August 6). The Annals of Applied Statistics.

Beyond Time-Homogeneity for Continuous-Time Multistate Markov Models

Kendall, E. B., Williams, J. P., Hermansen, G. H., Bois, F., & Thanh, V. H. (2024, August 8). Journal of Computational and Graphical Statistics.

Decision Theory via Model-Free Generalized Fiducial Inference

Williams, J. P., & Liu, Y. (2024, January 1). Lecture Notes in Computer Science.

Large-Sample Theory for Inferential Models: A Possibilistic Bernstein–von Mises Theorem

Martin, R., & Williams, J. P. (2024, January 1). Lecture Notes in Computer Science.

Neil Dey, Ryan Martin, and Jonathan P. Williams’ contribution to the Discussion of ‘Safe Testing’ by Grünwald, de Heide, and Koolen

Dey, N., Martin, R., & Williams, J. P. (2024, June 27). Journal of the Royal Statistical Society Series B (Statistical Methodology).

Prior exposure to racial discrimination and patterns of acute parasympathetic nervous system responses to a race‐related stress task among Black adults

Volpe, V. V., Kendall, E. B., Collins, A. N., Graham, M. G., Williams, J. P., & Holochwost, S. J. (2024, October 25). Psychophysiology, Vol. 10.

Word Embeddings as Statistical Estimators

Dey, N., Singer, M., Williams, J. P., & Sengupta, S. (2024, May 9). Sankhya B, Vol. 5.

A penalized complexity prior for deep Bayesian transfer learning with application to materials informatics

Abba, M. A., Williams, J. P., & Reich, B. J. (2023, October 31). The Annals of Applied Statistics, Vol. 17, pp. 3241–3256.

Generalized fiducial factor: An alternative to the Bayes factor for forensic identification of source problems

Williams, J. P., Ommen, D. M., & Hannig, J. (2023, January 24). The Annals of Applied Statistics.

The EAS approach to variable selection for multivariate response data in high-dimensional settings

Koner, S., & Williams, J. P. (2023, January 1). Electronic Journal of Statistics.

The EAS approach for graphical selection consistency in vector autoregression models

Williams, J. P., Xie, Y., & Hannig, J. (2022, September 27). Canadian Journal of Statistics.

Can Administrative Health Data Improve the Gold Standard? Evidence from a Model of the Progression of Myocardial Infarction

Nghiem, S., Williams, J., Afoakwah, C., Huynh, Q., Ng, S.-kay, & Byrnes, J. (2021, July 10). International Journal of Environmental Research and Public Health.

Discussion of “A Gibbs Sampler for a Class of Random Convex Polytopes”

Williams, J. P. (2021, July 3). Discussion of “A Gibbs Sampler for a Class of Random Convex Polytopes.” Journal of the American Statistical Association.

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.