Spring 2018

We meet on Wednesdays from 1:30-2:30 in Padelford C-301, which is the Statistics conference room. We do not have a fixed theme this quarter, so presenters are free to pick the topic of their choice. Some potential papers are listed below, and more will be added as we go along.

Schedule

The following is the current schedule of topics and presenters. We will continue the sign up process over the first few meetings of the quarter.

Date

Speaker

Topic
April 4 Johnny Paige Topics from Chpt 8 of Cressie and Wikle (2011)
April 11 Serge Aleshin-Guendel SPDE's for approximating Gaussian fields -- Simpson et al 2012 [Spatial Stats version] [arXiv version] Slides from talk are here: [Slides]
April 18 John Best Fuglstad et al 2017 paper on complexity priors [PDF]
April 25 Johnny Paige Paper on how to solve Matern SPDE [PDF]
May 2 Peter Gao and Max Schneider A tale of two papers: [Paper 1] [Paper 2]
May 9 Megan Ferguson Sampling and abundance estimation for ephemeral aggregations bowhead whales
May 16 Jim Faulkner SPDE approach for spatial point processes [Paper] [Slides] code from tutorial to come...
May 23 Max Schneider Estimation of spatial-temporal point process parameters [Paper]
May 30 Peter Gao Multiresolution Gaussian process models [Paper]

Resources

Here are some potential papers of interest

SPDE's and INLA

  • Bakka, H. 2018. How to solve the stochastic partial differential equation that gives a Matern random field using the finite element method. arXiv:1803.03765v1 [stat.ME] [PDF]
  • Bivand, R., V. Gomez-Rubio, and H. Rue. 2015. Spatial data analysis with R-INLA with some extensions. Journal of Statistical Software 63(20):1-31. Page with PDF and supplementary code
  • Blangiardo, M., and M., Cameletti. 2015. Spatial and spatio-temporal Bayesian models with R-INLA. John Wiley and Sons, Ltd. E-Book with UW access
  • 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
  • Lindgren, F., and H. Rue. 2015. Bayesian spatial modelling with R-INLA. Journal of Statistical Software 63(19):1-25. Page with PDF and supplementary code
  • Rue, H., S. Martino, and N. Chopin. 2009. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society, Series B 71(2):319-392. PDF
  • Simpson, D, F. Lindgren, and H. Rue. 2012. Think continuous: Markovian Gaussian models in spatial statistics. Spatial Statistics 1:16-29. [Spatial Stats version] [arXiv version]
  • 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

Prior selection

  • Fuglstad, G., Simpson, D., F. Lindgren, and H. Rue. 2017. Constructing priors that penalize the complexity of Gaussian random fields. arXiv:1503.00256v4 [stat.ME] . [PDF]

Uncertainty quantification and preferential sampling

  • Bolin, D., and F. Lindgren. 2015. Excursion and contour uncertainty regions for latent Gaussian models. Journal of the Royal Statistical Society, Series B 77(1):85-106. [Link]
  • Diggle, P.J., R. Menezes, and T. Su. 2010. Geostatistical inference under preferential sampling. Journal of the Royal Statistical Society. Series C (Applied Statistics) 59(2):191-232 [Link]
  • Gelfand, A., S.K. Sahu, and D.M. Holland. 2012. On the effect of preferential sampling on spatial prediction. Environmetrics 23(7):565-578. [Link]

Modeling large data sets

  • Banerjee, S., A.E. Gelfand, A.O. Finley, and H. Sang. 2008. Gaussian predictive process models for large spatial data sets. Journal of the Royal Statistical Society, Series B. 70(4):825-848. Link
  • Cressie, N., and G. Johannesson. 2008. Fixed rank kriging for very large spatial data sets. Journal of the Royal Statistical Society, Series B. 70(1):209-226. Link
  • Datta, A., S. Banerjee, A. O. Finley, and A. E. Gelfand. 2016. On nearest-neighbor Gaussian process models for massive spatial data. Computational Statistics 8(5):162-171. Link
  • Furrer, R., M. G. Genton, and D. Nychka. 2006. Covariance tapering for interpolation of large spatial datasets. Journal of Computational and Graphical Statistics 15(3):502-523. Link
  • Heaton et al. 2017. Methods for analyzing large spatial data: a review and comparison. arXiv:1710.05013v1 [stat.ME] -- PDF
  • Hughes, J., and M. Haran. 2013. Dimension reduction and alleviation of confounding for spatial generalized linear mixed models. Journal of the Royal Statistical Society, Series B. 75(1):139-159. PDF
  • Katzfuss, M. 2012. Bayesian non-stationary spatial modeling for very large data sets. Environmetrics 23(1):94-107Link
  • Katzfuss, M. 2016. A multi-resolution approximation for massive spatial datasets. Journal of the American Statistical Association (just accepted). Link
  • Nychka, D., S. Bandyapadhyay, D. Hammerling, F. Lindgren, and S. Sain. 2014. A multi-resolution Gaussian process model for the analysis of large spatial data sets. Journal of Computational and Graphical Statistics 24(2):579-599. Link (Note: This should ideally be presented with the LatticeKrig R package)
  • Simpson, D., F. Lindgren, and H. Rue. 2012. In order to make spatial statistics computationally feasible, we need to forget about the covariance function. Environmetrics 23:65-74. PDF
  • Stein, M. L., J. Chen, and M. Anitescu. 2013. Stochastic approximation of score functions for Gaussian processes. The Annals of Applied Statistics 7(2): 1162-1191. PDF
  • Sun, Y., and M. L. Stein. 2016. Statistically and computationally efficient estimating equations for large spatial datasets. Journal of Computational and Graphical Statistics 25(1):187-208. Link

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
  • 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 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