Spring 2017

We meet on Tuesdays from 2:00-3:00 in Padelford C-301. Our themes for this quarter are non-stationarity and non-normality. However, feel free to deviate from these if you wish.

Schedule

The following is the current schedule of papers and presenters. We will sign up for presentation slots during our first meeting and as necessary thereafter.

Date

Speaker

Topic
April 11 Johnny Paige Introduction to non-normality [Slides]
April 18 John Best Introduction to non-stationarity -- Chapter 9 in Handbook of Spatial Statistics [Slides]
April 25 Hannah Director Regression-based covariance functions for nonstationary spatial modeling - Risser and Calder 2015
May 2 Jim Faulkner Geostatistical modelling using non-Gaussian Matern fields. Wallin and Bolin 2015 -- [Slides]
May 9 No Meeting
May 16 Branden Olson Diggle et al. 2013 - Spatial and spatio-temporal log-Gaussian Cox processes: extending the geostatistical paradigm
May 23 Max Schneider Fuglstad et al. 2015 - Does non-stationary spatial data always require non-stationary random fields?
May 30 TBD
June 6 No meeting

Potential Papers for Discussion

These are only a small sample of possible papers. Feel free to pick one not on the list. This list will be appended throughout the quarter...

Non-Normality

  • Bolin, D. 2014. Spatial Matern fields driven by non-Gaussian noise. Scandinavian Journal of Statistics 41(3):557-579. Link
  • Diggle, P. J., P. Moraga, B. Rowlingson, and B. M. Taylor. 2013. Spatial and spatio-temporal log-Gaussian Cox processes: extending the geostatistical paradigm. Statistical Science 28(4):542-563. Link
  • Duan, J. A., M. Guindani, and A. E. Gelfand. 2007. Generalized spatial Dirichlet process models. Biometrika 94(4):809-825. Link
  • Gelfand, A. E., A. Kottas, and S. N. MacEachern. 2005. Bayesian nonparametric spatial modeling with Dirichlet process mixing. Journal of the American Statistical Association 100:1021-1035. Link
  • Hughes, J., and M. Haran. 2012. Dimension reduction and alleviation of confounding for spatial generalized linear mixed models. Journal of the Royal Statistical Society, Series B 42(3):872-890 Link
  • Palacios, M. B., and M. F. J. Steel. 2006. Non-Gaussian Bayesian geostatistical modelling. Journal of the American Statistical Association 101:604-618 Link
  • Simpson, D., J. B. Illian, F. Lindgren, S. H. Sorbye, and H. Rue. 2016. Going off the grid: computationally efficient inference for log-Gaussian Cox processes. Biometrika 103(1):49-70. Link
  • Wallin, J., and D. Bolin. 2015. Geostatistical modelling using non-Gaussian Matern fields. Scandinavian Journal of Statistics 42(3):872-890. Link

Non-Stationarity

  • Fuglstad, G., F. Lindgren, D. Simpson, and H. Rue. 2015. Exploring a new class of non-stationary spatial Gaussian random fields with varying local anisotropy. Statistica Sinica 25(1):115-133. Link
  • Fuglstad, G., D. Simpson, F. Lindgren, and H. Rue. 2015. Does non-stationary spatial data always require non-stationary random fields? Spatial Statistics 14:505-531. Link
  • Heaton, M. J., W. F. Christensen, and M. A. Terres. 2017. Nonstationary Gaussian process models using spatial hierarchical clustering from finite differences. Technometrics 59(1):93-101. Link
  • Ingebrigtsen, R., F. Lindgren, and I. Steinsland. 2014. Spatial models with explanatory variables in the dependence structure.Spatial Statistics 8:20-38. Link
  • Neto, J. H. V., A. M. Schmidt, and P. Guttorp. 2014. Accounting for spatially varying directional effects in spatial covariance structures. Jounal of the Royal Statistical Society, Series C: Applied Statistics 63(1):103-122. Link
  • Risser, M. D., and C. A. Calder. 2015. Regression-based covariance functions for nonstationary spatial modeling. Environmetrics 26(4):284-297. Link
  • Yue, Y., and P. L. Speckman. 2010. Nonstationary spatial Gaussian Markov random fields. Journal of Computational and Graphical Statistics 19(1):96-116. Link

Additional Papers

  • Lindgren, F., and H. Rue. 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 73(4):423-498. PDF

Additional Resources

  • Cressie, N., and C. K. Wikle. 2011. Statistics for spatial-temporal data. John Wiley & Sons
  • Banerjee, S., B. P. Carlin, and A. E. Gelfand. 2015. Hierarchical modeling and analysis for spatial data. . Second edition. CRC Press, Taylor & Francis Group.
  • Gelfand, A. E., P. Diggle, P. Guttorp, and M. Fuentes (Eds.). 2010. Handbook of spatial statistics. CRC Press.
  • Rue, H., and L. Held. 2005. Gaussian Markov random fields: theory and applications. Chapman & Hall/CRC.

Assorted Space-Time Links