Clemson ENVR Working Group

Contact Information

Useful Resources

• SAMSI Year-Long Program on Mathematical and Statistical Methods for Climate and the Earth System (CLIM)
• ASA Section on Statistics and the Environment
• NOAA's National Centers for Environmental Information
• National Hurricane Center
• CBMS Regional Conference in the Mathematical Sciences: Spatial and Spatio-temporal Data

References

Papers

• Cressie, N. (1990). The origins of kriging. Mathematical geology, 22(3), 239-252.
• Diggle, P. J., Tawn, J. A., & Moyeed, R. A. (1998). Model‐based geostatistics. Journal of the Royal Statistical Society: Series C (Applied Statistics), 47(3), 299-350.
• Rue, H., Martino, S., & Chopin, N. (2009). Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the royal statistical society: Series b (statistical methodology), 71(2), 319-392.
• Lindgren, F., Rue, H., & Lindström, J. (2011). An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73(4), 423-498.
• Stein, M. L. (2005). Space–time covariance functions. Journal of the American Statistical Association, 100(469), 310-321.
• Stein, M. L., Chi, Z., & Welty, L. J. (2004). Approximating likelihoods for large spatial data sets. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 66(2), 275-296.
• Stein, M. L. (2005). Statistical methods for regular monitoring data. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(5), 667-687.
• Haslett, J., & Raftery, A. E. (1989). Space‐time modelling with long‐memory dependence: Assessing Ireland's wind power resource. Journal of the Royal Statistical Society: Series C (Applied Statistics), 38(1), 1-21.
• Cressie, N., & Johannesson, G. (2008). Fixed rank kriging for very large spatial data sets. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 70(1), 209-226.
• Banerjee, S., Gelfand, A. E., Finley, A. O., & Sang, H. (2008). Gaussian predictive process models for large spatial data sets. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 70(4), 825-848.
• Davison, A. C., & Smith, R. L. (1990). Models for exceedances over high thresholds. Journal of the Royal Statistical Society: Series B (Methodological), 52(3), 393-425.
• Katz, R. W., Parlange, M. B., & Naveau, P. (2002). Statistics of extremes in hydrology. Advances in water resources, 25(8-12), 1287-1304.
• Berrocal, V. J., Gelfand, A. E., & Holland, D. M. (2010). A spatio-temporal downscaler for output from numerical models. Journal of agricultural, biological, and environmental statistics, 15(2), 176-197.
• Genest, C., & Favre, A. C. (2007). Everything you always wanted to know about copula modeling but were afraid to ask. Journal of hydrologic engineering, 12(4), 347-368.

Books

• Gelfand, A. E., Diggle, P., Guttorp, P., & Fuentes, M. (Eds.). (2010). Handbook of spatial statistics. CRC press.
• Cressie, N. (1993). Statistics for spatial data. John Wiley & Sons.
• Stein, M. L. (1999). Interpolation of spatial data: some theory for kriging. Springer Science & Business Media.
• Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2004). Hierarchical modeling and analysis for spatial data. CRC press.
• Rue, H., & Held, L. (2005). Gaussian Markov random fields: theory and applications. CRC press.
• Cressie, N., & Wikle, C. K. (2015). Statistics for spatio-temporal data. John Wiley & Sons.
• Wikle, C. K., Zammit-Mangion, A., & Cressie, N. (2019). Spatio-temporal Statistics with R. CRC Press.
• Coles, S. (2001). An introduction to statistical modeling of extreme values (Vol. 208, p. 208). London: Springer.
• De Haan, L., & Ferreira, A. (2006). Extreme value theory: an introduction. Springer Science & Business Media.
• Ramsay, J., & Silverman, B. W. (2005). Functional data analysis (Springer series in statistics).
• Dryden, I. L., & Mardia, K. V. (2016). Statistical shape analysis: with applications in R (Vol. 995). John Wiley & Sons.
• Joe, H. (1997). Multivariate models and multivariate dependence concepts. CRC Press.
• Nelsen, R. B. (2007). An introduction to copulas. Springer Science & Business Media.

2021 Spring Schedule

2020 Fall Schedule