Henry R Scharf

Interests

Research

Bayesian statistics; Spatio-temporal statistics; Computational statistics; Ecological and environmental applications

Courses

Scholarly Contributions

Journals/Publications

• Boulil, Z. L., Durban, J. W., Fearnbach, H., Joyce, T. W., Leander, S. G., & Scharf, H. R. (2023). Detecting changes in dynamic social networks using multiply-labeled movement data. Journal of Agricultural, Biological and Environmental Statistics, 28(2), 243--259.
• Scharf, H., Schierbaum, J., Matsumoto, H., & Assal, T. (2023). Predicting fine-scale taxonomic variation in landscape vegetation using large satellite imagery data sets. arXiv preprint arXiv:2309.10325.
• Williams, P. J., Lu, X., Scharf, H. R., & Hooten, M. B. (2023). Embracing asymmetry in nature: How to account for skewness in ecological data. Ecological Informatics, 75, 102085.
• Raiho, A. M., Scharf, H. R., Roland, C. A., Swanson, D. K., Stehn, S. E., & Hooten, M. B. (2022). Searching for refuge: A framework for identifying site factors conferring resistance to climate-driven vegetation change. Diversity and Distributions, 28(4), 793--809.
• Scharf, H. (2022). Local Indicators of Spatial Association (LISA). Wiley StatsRef: Statistics Reference Online, 1--9.
• Scharf, H. R., Lu, X., Williams, P. J., & Hooten, M. B. (2022). Constructing flexible, identifiable and interpretable statistical models for binary data. International Statistical Review, 90(2), 328--345.
• Scharf, H. R., Raiho, A. M., Pugh, S., Roland, C. A., Swanson, D. K., Stehn, S. E., & Hooten, M. B. (2022). Multivariate Bayesian clustering using covariate-informed components with application to boreal vegetation sensitivity. Biometrics, 78(4), 1427--1440.
• Reimer JR, ., Arroyo-Esquivel, J., Jiang, J., Scharf, H. R., Wolkovich, E. M., Zhu, K., & Boettiger, C. (2021). Noise can create or erase long transient dynamics. Theoretical Ecology, 14(4), 685--695.
• Scharf, H. (2021). Statistical Analysis of Animal Movement: Understanding Behavior Through Hierarchical Parametric Models. NOTICES OF THE AMERICAN MATHEMATICAL SOCIETY, 68(6).
• Scharf, H. R., & Buderman, F. E. (2020). Animal movement models for multiple individuals. Wiley Interdisciplinary Reviews: Computational Statistics, 12(6), e1506.
• Hooten, M. B., Scharf, H. R., & Morales, J. M. (2019). Running on empty: recharge dynamics from animal movement data. Ecology letters, 22(2), 377--389.
• Scharf, H. R., Hooten, M. B., Wilson, R. R., Durner, G. M., & Atwood, T. C. (2019). Accounting for phenology in the analysis of animal movement. Biometrics, 75(3), 810--820.
• Hooten, M. B., Scharf, H. R., Hefley, T. J., Pearse, A. T., & Weegman, M. D. (2018). Animal movement models for migratory individuals and groups. Methods in Ecology and Evolution, 9(7), 1692--1705.
• Scharf, H. R., Hooten, M. B., Johnson, D. S., & Durban, J. W. (2018). Process convolution approaches for modeling interacting trajectories. Environmetrics, 29(3), e2487.
• Hefley, T. J., Broms, K. M., Brost, B. M., Buderman, F. E., Kay, S. L., Scharf, H. R., Tipton, J. R., Williams, P. J., & Hooten, M. B. (2017). The basis function approach for modeling autocorrelation in ecological data. Ecology, 98(3), 632--646.
• Scharf, H., Hooten, M. B., & Johnson, D. S. (2017). Imputation approaches for animal movement modeling. Journal of Agricultural, Biological and Environmental Statistics, 22, 335--352.
• Scharf, H., Hooten, M., Fosdick, B. K., Johnson, D. S., London, J. M., & Durban, J. W. (2016). Dynamic social networks based on movement. The Annals of Applied Statistics, 10(4), 2182--2202.