Henry Scharf

I am an Assistant Professor of Statistics in the Department of Mathematics at the University of Arizona. I am originally from Tucson and happy to be back after spending 11 years studying and working around the Southwest. Before starting at the UofA in Fall 2023, I was an assistant professor at San Diego State University for four years. I am trained as a statistician (PhD, Colorado State University), a mathematician, (BS in Math and Physics, UofA) and an educator (Masters in Education, UofA). My research focuses primarily on the development of new methods for the study of stochastic processes and networks, especially for spatio-temporal applications in ecology. I also work on developing new computational tools that provide approximate statistical inferential procedures for large data sets. My students and I make up the Toyon Statistical Lab.

If you would like to work with me, please send me an email with a brief description of your research interests. We'll set up a meeting to chat.

Scharf, H. R., J. Schierbaum^*, H. Matsumoto, T. Assal (2024). Predicting species-level vegetation cover using large satellite imagery data sets. Journal of Agricultural, Biological and Environmental Statistics.

McFadden A. J.^*, A. D. Stow, P. J. Riggan, R. Tissell, J. O'Leary, H. R. Scharf (2024). Estimating Fire Radiative Energy Density with Repeat-Pass Aerial Thermal-Infrared Imaging of Actively Progressing Wildfires. Fire, 7(6):179.

Williams, P. J., X. Lu, H. R. Scharf, M. B. Hooten (2023). Embracing asymmetry in nature: How to account for skewness in ecological data. Ecological Informatics, 75: 102085.

Boulil, Z. L.^*, J. W. Durban, H. Fearnbach, T. W. Joyce, S. G. M. Leander, H. R. Scharf (2023). Detecting changes in dynamic social networks using multiply-labeled movement data. Journal of Agricultural, Biological and Environmental Statistics, 28: 243–259.

Scharf, H. R. (2022). Local indicators of spatial association (LISA). Wiley StatsRef: Statistics Reference Online.

Scharf, H. R., X. Lu, P. J. Williams, M. B. Hooten (2022). Constructing flexible, identifiable and interpretable statistical models for binary data. International Statistical Review, 90: 328–345.

Raiho, A. M., H. R. Scharf, C. A. Roland, D. K. Swanson, S. E. Stehn, and M. B. Hooten (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. R. A. M. Raiho, S. Pugh, C. A. Roland, D. K. Swanson, S. E. Stehn, M. B. Hooten (2021). Multivariate Bayesian clustering using covariate-informed components with application to boreal vegetation sensitivity. Biometrics, 78: 1427–1440.

Reimer, J. R., J. Arroyo-Esquivel, J. Jiang, H. R. Scharf, E. M. Wolkovich, K. Zhu, C. Boettiger (2021). Noise can create or erase long transient dynamics. Theoretical Ecology, 14: 685–695.

Scharf, H. R. (2021). Statistical analysis of animal movement: Understanding behavior through hierarchical parametric models. Notices of the American Mathematical Society, 68(6), 911–924.

Scharf, H. R., F. Buderman (2020). Animal movement models for multiple individuals. Wiley Interdisciplinary Reviews: Computational Statistics, e1506.

Scharf, H. R., M. B. Hooten, R. R. Wilson, G. M. Durner, T. C. Atwood (2019). Accounting for phenology in the analysis of animal movement. Biometrics, 75: 810–820.

Hooten, M. B, H. R. Scharf, J. M. Morales (2019). Running on empty: Recharge dynamics from animal movement data. Ecology Letters, 22, 377–389.

Hooten, M. B., H. R. Scharf, T. J. Hefley, A. T. Pearse, M. D. Weegman (2018). Animal movement models for migratory individuals and groups. Methods in Ecology and Evolution, 9, 1692–1705.

Scharf, H. R., M. B. Hooten, D. S. Johnson, J. W. Durban (2018). Process convolution approaches for modeling interacting trajectories. Environmetrics, e2487.

Scharf, H. R., M. B. Hooten, D. S. Johnson (2017). Imputation approaches for animal movement modeling. Journal of Agricultural, Biological and Environmental Statistics, 22(3), 335–352.

Hefley, T. J., K. M. Broms, B. M. Brost, F. E. Buderman, S. L. Kay, H. R. Scharf, J. R. Tipton, P. J. Williams, and M. B. Hooten. (2017). The basis function approach to modeling autocorrelation in ecological data. Ecology, 98(3), 632–646. Associated Shiny Web Application.

Scharf, H. R., M. B. Hooten, B. K. Fosdick, D. S. Johnson, J. M. London, and J. W. Durban. (2016). Dynamic social networks based on movement. Annals of Applied Statistics, 10(4), 2182–2202.

2024
(University of Arizona) MATH/STAT 574E: Environmental Statistics
(University of Arizona) MATH/STAT 571B: Design of Experiments
2023
(University of Arizona) MATH/STAT 571A: Advanced Statistical Regression Analysis
(San Diego State University) STAT 596: Spatio-temporal Analysis and Modeling
(San Diego State University) STAT 410: R Programming and Data Science
2022
(San Diego State University) STAT 580: Statistical Computing
(San Diego State University) STAT 410: R Programming and Data Science
(San Diego State University) STAT 676: Bayesian Statistics
2021
(San Diego State University) STAT 580: Statistical Computing
(San Diego State University) STAT 596: Spatio-temporal Analysis and Modeling
2020
(San Diego State University) STAT 580: Statistical Computing
(San Diego State University) STAT 696: Applied Spatio-temporal Statistics
2019
(San Diego State University) STAT 596: Introduction to Statistical Learning
International Association for Landscape Ecology annual conference 2019: Workshop Co-Instructor Statistical Models for Animal Movement (email for workshop materials)
2018
International Statistical Ecology Conference 2018: Workshop Co-Instructor Statistical Models for Animal Movement (email for workshop materials)
2015
Conference on Statisticsal Practice 2015: Tutorial on Parallel Computing in R foreach package Slides, Handout, R script, & Bikeshare data parallel package Slides & R script Hadoop Slides, Handout, Lab Instructions, & R script
2013
(Colorado State University) STAT 204: Statistics for Business Students

Scharf, H. R., K. Dinh^*, H. Rosales-Portillo^*, A. Rivera^* (2023). anipaths: Animation of observed trajectories using spline- or state-space model-based interpolation. R package version 0.10.2.

Movement of polar bears in the Chukchi and Beaufort seas.

Scharf, H. R., M. B. Hooten, R. R. Wilson, G. M. Durner, T. C. Atwood (2019). Accounting for phenology in the analysis of animal movement. Biometrics, 75: 810–820.

Movement of killer whales near the Antarctic peninsula.

Scharf, H. R., M. B. Hooten, B. K. Fosdick, D. S. Johnson, J. M. London, and J. W. Durban. (2016). Dynamic social networks based on movement. Annals of Applied Statistics. 10(4), 2182–2202.

Movement of Greenland White Fronted Geese on the east coast of Ireland.

Hooten, M. B., H. R. Scharf, T. J. Hefley, A. T. Pearse, M. D. Weegman (2018). Animal movement models for migratory individuals and groups. Methods in Ecology and Evolution. 9, 1692–1705.