Bibliography

Some useful papers, books etc on the topics covered in the course.

Things can change in mgcv land very quickly, check the package changelog on CRAN whenever you update.

Course materials

  • Materials for a variant of this course, given at the Ecological Society of America Annual Meeting 2016 (with Eric Pedersen, Gavin Simpson, Noam Ross). Course website.
  • Course notes from spatial modelling course at Duke (with Jason Roberts). Course website

Books

  • Wood, S. N. (2006). Generalized Additive Models. CRC Press.
  • Ruppert, D., Wand, M. P., & Carroll, R. J. (2003). Semiparametric Regression. Cambridge University Press.
  • Hastie, T. J., & Tibshirani, R. J. (1990). Generalized Additive Models. Taylor & Francis.

General

  • Wood, S. N. (2003). Thin plate regression splines. Journal of the Royal Statistical Society: Series B (Statistical Methodology).
  • Reiss, P. T., & Ogden, R. T. (2009). Smoothing parameter selection for a class of semiparametric linear models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 71(2), 505–523.
  • Wood, S. N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73(1), 3–36. http://doi.org/10.1111/j.1467-9868.2010.00749.x
  • Wood, S. N., Pya, N., & Säfken, B. (2016). Smoothing parameter and model selection for general smooth models. Journal of the American Statistical Association, 1–45. http://doi.org/10.1080/01621459.2016.1180986

Uncertainty

Model checking

Model selection

  • Marra, G. and Wood, S.N. (2011) Practical variable selection for generalized additive models. Computational Statistics and Data Analysis, 55, 2372–2387. http://doi.org/10.1016/j.csda.2011.02.004
  • Nychka, D. (1988) Bayesian Confidence Intervals for Smoothing Splines. Journal of the American Statistical Association, 83, 1134–1143. http://www.jstor.org/stable/2290146
  • Wood, S.N. (2013) A simple test for random effects in regression models. Biometrika, 100, 1005–1010. doi:10.1093/biomet/ast038
  • Wood, S.N. (2013) On p-values for smooth components of an extended generalized additive model. Biometrika, 100, 221–228. doi:10.1093/biomet/ass048

Response distributions

Big data sets

  • Wood, S.N., Goude, Y. and Shaw, S. (2015) Generalized additive models for large data sets. Journal of the Royal Statistical Society: Series C (Applied Statistics), 64, 139–155.
  • Wood, S.N., Li, Z., Shaddick, G. and Augustin, N.H. (2016) Generalized additive models for gigadata: modelling the UK black smoke network daily data. Journal of the American Statistical Association, 1–40.

Applications

Species distribution modelling

  • Augustin, N. H., Musio, M., Wilpert, von, K., Kublin, E., Wood, S. N., & Schumacher, M. (2009). Modeling Spatiotemporal Forest Health Monitoring Data. Journal of the American Statistical Association, 104(487), 899–911. http://doi.org/10.1198/jasa.2009.ap07058
  • Augustin, N. H., Trenkel, V. M., Wood, S. N., & Lorance, P. (2013). Space-time modelling of blue ling for fisheries stock management. Environmetrics, 24(2), 109–119. http://doi.org/10.1002/env.2196
  • Miller, D. L., Burt, M. L., Rexstad, E. A., & Thomas, L. (2013). Spatial models for distance sampling data: recent developments and future directions. Methods in Ecology and Evolution, 4(11), 1001–1010. http://doi.org/10.1111/2041-210X.12105
  • Wood, S. N., Bravington, M. V., & Hedley, S. L. (2008). Soap film smoothing. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 70(5), 931–955. http://doi.org/10.1111/j.1467-9868.2008.00665.x