Erin Schliep

North, J. S., Wikle, C. K., & Schliep, E. M. (2023, September 29). A Review of Data-Driven Discovery for Dynamic Systems. INTERNATIONAL STATISTICAL REVIEW, Vol. 9. https://doi.org/10.1111/insr.12554

North, J. S., Schliep, E. M., Hansen, G. J. A., Kundel, H., Custer, C. A., Mclaughlin, P., & Wagner, T. (2023, November 28). Accounting for spatiotemporal sampling variation in joint species distribution models. JOURNAL OF APPLIED ECOLOGY, Vol. 11. https://doi.org/10.1111/1365-2664.14547

Wagner, T., Schliep, E. M., North, J. S., Kundel, H., Custer, C. A., Ruzich, J. K., & Hansen, G. J. A. (2023). Predicting climate change impacts on poikilotherms using physiologically guided species abundance models. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 120(15). https://doi.org/10.1073/pnas.2214199120

North, J. S., Wikle, C. K., & Schliep, E. M. (2022). A Bayesian Approach for Data-Driven Dynamic Equation Discovery. Journal of Agricultural, Biological and Environmental Statistics, 8. https://doi.org/10.1007/s13253-022-00514-1

Mirzaee, A., McGarvey, R. G., Aguilar, F. X., & Schliep, E. M. (2022). Impact of biopower generation on eastern US forests. Environment, Development and Sustainability, 3. https://doi.org/10.1007/s10668-022-02235-4

Schliep, E. M., Schafer, T. L. J., & Hawkey, M. (2021). Distributed lag models to identify the cumulative effects of training and recovery in athletes using multivariate ordinal wellness data. Journal of Quantitative Analysis in Sports, 17(3), 241–254. https://doi.org/10.1515/jqas-2020-0051

Schliep, E. M., Gelfand, A. E., Abaurrea, J., Asín, J., Beamonte, M. A., & Cebrián, A. C. (2021). Long‐term spatial modelling for characteristics of extreme heat events. Journal of the Royal Statistical Society: Series A (Statistics in Society), 184(3), 1070–1092. https://doi.org/10.1111/rssa.12710

Bailey, S. N., Elliott, G. P., & Schliep, E. M. (2021). Seasonal temperature–moisture interactions limit seedling establishment at upper treeline in the Southern Rockies. Ecosphere, 12(6). https://doi.org/10.1002/ecs2.3568

Cebrián, A. C., Asín, J., Gelfand, A. E., Schliep, E. M., Castillo-Mateo, J., Beamonte, M. A., & Abaurrea, J. (2021). Spatio-temporal analysis of the extent of an extreme heat event. Stochastic Environmental Research and Risk Assessment, 36(9), 2737–2751. https://doi.org/10.1007/s00477-021-02157-z

Schliep, E. M., Collins, S. M., Rojas-Salazar, S., Lottig, N. R., & Stanley, E. H. (2020). Data fusion model for speciated nitrogen to identify environmental drivers and improve estimation of nitrogen in lakes. The Annals of Applied Statistics, 14(4). https://doi.org/10.1214/20-aoas1371

Soranno, P. A., Cheruvelil, K. S., Liu, B., Wang, Q., Tan, P. N., Zhou, J., … Webster, K. E. (2020). Ecological prediction at macroscales using big data: Does sampling design matter? Ecological Applications, 30(6). https://doi.org/10.1002/eap.2123

Wagner, T., Hansen, G. J. A., Schliep, E. M., Bethke, B. J., Honsey, A. E., Jacobson, P. C., … White, S. L. (2020). Improved understanding and prediction of freshwater fish communities through the use of joint species distribution models. Canadian Journal of Fisheries and Aquatic Sciences, 77(9), 1540–1551. https://doi.org/10.1139/cjfas-2019-0348

North, J. S., Schliep, E. M., & Wikle, C. K. (2020). On the spatial and temporal shift in the archetypal seasonal temperature cycle as driven by annual and semi‐annual harmonics. Environmetrics, 32(6). https://doi.org/10.1002/env.2665

Stanley, E. H., Rojas‐Salazar, S., Lottig, N. R., Schliep, E. M., Filstrup, C. T., & Collins, S. M. (2019). Comparison of total nitrogen data from direct and Kjeldahl‐based approaches in integrated data sets. Limnology and Oceanography: Methods, 17(12), 639–649. https://doi.org/10.1002/lom3.10338

Bartley, M. L., Hanks, E. M., Schliep, E. M., Soranno, P. A., & Wagner, T. (2019). Identifying and characterizing extrapolation in multivariate response data. PLOS ONE, 14(12), e0225715. https://doi.org/10.1371/journal.pone.0225715

Wagner, T., Lottig, N. R., Bartley, M. L., Hanks, E. M., Schliep, E. M., Wikle, N. B., … Zhou, J. (2019). Increasing accuracy of lake nutrient predictions in thousands of lakes by leveraging water clarity data. Limnology and Oceanography Letters, 5(2), 228–235. https://doi.org/10.1002/lol2.10134

Ramseyer Winter, V., Landor, A. M., Teti, M., Morris, K., Schliep, E. M., Pevehouse-Pfeiffer, D., & Pekarek, E. (2019). Is body appreciation a mechanism of depression and anxiety? An investigation of the 3-Dimensional Body Appreciation Mapping (3D-BAM) intervention. Mental Health & Prevention, 14, 200158. https://doi.org/10.1016/j.mph.2019.200158

Schliep, E. M., & Gelfand, A. E. (2019). Velocities for spatio-temporal point patterns. Spatial Statistics, 29, 204–225. https://doi.org/10.1016/j.spasta.2018.12.007

Schliep, E. M., Gelfand, A. E., Mitchell, R. M., Aiello‐Lammens, M. E., & Silander, J. A., Jr. (2018). Assessing the joint behaviour of species traits as filtered by environment. Methods in Ecology and Evolution, 9(3), 716–727. https://doi.org/10.1111/2041-210x.12901

Lany, N. K., Zarnetske, P. L., Schliep, E. M., Schaeffer, R. N., Orians, C. M., Orwig, D. A., & Preisser, E. L. (2018). Asymmetric biotic interactions and abiotic niche differences revealed by a dynamic joint species distribution model. Ecology, 99(5), 1018–1023. https://doi.org/10.1002/ecy.2190

Gelfand, A. E., & Schliep, E. M. (2018). Bayesian Inference and Computing for Spatial Point Patterns. In NSF-CBMS Regional Conference Series in Probability and Statistics. https://doi.org/10.1214/cbms/1530065028

Wagner, T., & Schliep, E. M. (2018). Combining nutrient, productivity, and landscape‐based regressions improves predictions of lake nutrients and provides insight into nutrient coupling at macroscales. Limnology and Oceanography, 63(6), 2372–2383. https://doi.org/10.1002/lno.10944

Schliep, E. M. (2018). Comments on: Process modeling for slope and aspect with application to elevation data maps. TEST, 27(4), 778–782. https://doi.org/10.1007/s11749-018-0620-4

Clark, J., Kueppers, L., Stover, D., Wyckoff, P., & Schliep, E. (2018). Disturbance and Vegetation Dynamics in Earth System Models: Workshop Report. https://doi.org/10.2172/1616531

Schliep, E. M., Gelfand, A. E., Clark, J. S., & Kays, R. (2018). Joint Temporal Point Pattern Models for Proximate Species Occurrence in a Fixed Area Using Camera Trap Data. Journal of Agricultural, Biological and Environmental Statistics, 23(3), 334–357. https://doi.org/10.1007/s13253-018-0327-8

Schliep, E. M., Lany, N. K., Zarnetske, P. L., Schaeffer, R. N., Orians, C. M., Orwig, D. A., … Guisan, A. (2018). Joint species distribution modelling for spatio‐temporal occurrence and ordinal abundance data. Global Ecology and Biogeography, 27(1), 142–155. https://doi.org/10.1111/geb.12666

Schliep, E. M., Gelfand, A. E., & Holland, D. M. (2017). Alternating Gaussian process modulated renewal processes for modeling threshold exceedances and durations. Stochastic Environmental Research and Risk Assessment, 32(2), 401–417. https://doi.org/10.1007/s00477-017-1417-9

Schliep, E. M., Gelfand, A. E., Clark, J. S., & Tomasek, B. J. (2017). Biomass prediction using a density-dependent diameter distribution model. The Annals of Applied Statistics, 11(1). https://doi.org/10.1214/16-aoas1007

Taylor-Rodríguez, D., Kaufeld, K., Schliep, E. M., Clark, J. S., & Gelfand, A. E. (2017). Joint Species Distribution Modeling: Dimension Reduction Using Dirichlet Processes. Bayesian Analysis, 12(4). https://doi.org/10.1214/16-ba1031

Gelfand, A. E., & Schliep, E. M. (2016). Spatial statistics and Gaussian processes: A beautiful marriage. Spatial Statistics, 18, 86–104. https://doi.org/10.1016/j.spasta.2016.03.006

Rundel, C. W., Schliep, E. M., Gelfand, A. E., & Holland, D. M. (2015). A data fusion approach for spatial analysis of speciated PM<sub>2.5</sub>across time. Environmetrics, 26(8), 515–525. https://doi.org/10.1002/env.2369

Schliep, E. M., Gelfand, A. E., & Holland, D. M. (2015). Autoregressive spatially varying coefficients model for predicting daily PM<sub>2.5</sub> using VIIRS satellite AOT. Advances in Statistical Climatology, Meteorology and Oceanography, 1(1), 59–74. https://doi.org/10.5194/ascmo-1-59-2015

Schliep, E. M., & Hoeting, J. A. (2015). Data augmentation and parameter expansion for independent or spatially correlated ordinal data. Computational Statistics & Data Analysis, 90, 1–14. https://doi.org/10.1016/j.csda.2015.03.020

Schliep, E. M., Gelfand, A. E., Clark, J. S., & Zhu, K. (2015). Modeling change in forest biomass across the eastern US. Environmental and Ecological Statistics, 23(1), 23–41. https://doi.org/10.1007/s10651-015-0321-z

Hanks, E. M., Schliep, E. M., Hooten, M. B., & Hoeting, J. A. (2015). Restricted spatial regression in practice: geostatistical models, confounding, and robustness under model misspecification. Environmetrics, 26(4), 243–254. https://doi.org/10.1002/env.2331

Schliep, E. M., Gelfand, A. E., & Clark, J. S. (2015). Stochastic Modeling for Velocity of Climate Change. Journal of Agricultural, Biological, and Environmental Statistics, 20(3), 323–342. https://doi.org/10.1007/s13253-015-0210-9

Schliep, E. M., Dong, T. Q., Gelfand, A. E., & Li, F. (2014). Modeling individual tree growth by fusing diameter tape and increment core data. Environmetrics, 25(8), 610–620. https://doi.org/10.1002/env.2324

Schliep, E. M., & Hoeting, J. A. (2013). Multilevel Latent Gaussian Process Model for Mixed Discrete and Continuous Multivariate Response Data. Journal of Agricultural, Biological, and Environmental Statistics, 18(4), 492–513. https://doi.org/10.1007/s13253-013-0136-z

Merrill, S. C., Walter, S. M., Peairs, F. B., & Schleip, E. M. (2013). The Distribution of European Corn Borer (Lepidoptera: Crambidae) Moths in Pivot-Irrigated Corn. Journal of Economic Entomology, 106(5), 2084–2092. https://doi.org/10.1603/ec12358

Schliep, E. M., Cooley, D., Sain, S. R., & Hoeting, J. A. (2010). A comparison study of extreme precipitation from six different regional climate models via spatial hierarchical modeling. Extremes, 13(2), 219–239. https://doi.org/10.1007/s10687-009-0098-2