Shand, L., A. Staid, E.L. Roesler, an K. Larson (2021). An observational study of the persistence of ship tracks using GOES-R satellite imagery and AIS ship positions. Environmental Data Science (In Preparation).

Tucker J.D., L. Shand, and K. Chowdhary (2021). Multimodal Bayesian Registration of Noisy Functions using Hamiltonian Monte Carlo. Computational Statistics and Data Analysis. DOI: 10.1016/j.csda.2021.107298

Patel, L., L. Shand, J.D. Tucker, and G. Huerta (2020). Assessing extreme value analysis to predict rare events from the global terrorism database. In JSM Proceedings, Section on Statistics in Defense and National Security. Alexandria, VA: American Statistical Association

Patel, L. and L. Shand (2020). Simulating cloud-aerosol interactions made by ship emissions. In JSM Proceedings, Section on Statistics and the Environment. Alexandria, VA: American Statistical Association

Harris, T., Tucker J.D., B. Li, and L. Shand (2020). Elastic depths for detecting shape anomalies in functional data. Technometrics. doi: 10.1080/00401706.2020.1811156

Tucker, J. D., L. Shand, J. R. Lewis (2018). Handling Missing Data in self-exciting point process models. Spatial Statistics. 29.160-176.

Guo S., M. A. Cooper., and L. Shand (2018). A statistical representation of pyrotechnic research igniter output. AIP Conference Proceedings. 1979 (1). doi: 10.1063/1.5044973

Shand, L. and B. Li (2018). Spatially Varying Autoregressive Models for Prediction of New HIV Diagnoses. Journal of the Royal Statistical Society: Series C.

Shand, L. and B. Li (2017). Modeling Nonstationarity in Space and Time. Biometrics.

Shand, L., W. M. Brown, L. F. Chaves, T. L. Goldberg, G. L. Hamer, L. Haramis, U. Kitron, E. D. Walker, and M.O. Ruiz (2016). Predicting West Nile Virus Infection Risk From the Synergistic Effects of Rainfall and Temperature. Journal of Medical Entomology. 1-10. doi: 10.1093/jme/tjw042.