Skip to main content
SpringerLink
Log in

Discussion on “Competition on Spatial Statistics for Large Datasets”

  • Published:
Journal of Agricultural, Biological and Environmental Statistics Aims and scope Submit manuscript

Abstract

The Competition on Spatial Statistics for Large Datasets ran in late 2020 and early 2021 and attracted several researchers in spatial statistics, including some in our group at the University of Wollongong, Australia. In this discussion paper, we first summarize our submission to the competition. We then discuss some aspects of the competition and give suggestions for future competitions with regard to the datasets and the assessment methods used.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Abdulah S, Ltaief H, Sun Y, Genton MG, Keyes DE (2018) ExaGeoStat: a high performance unified software for geostatistics on manycore systems. IEEE Trans Parallel Distrib Syst 29:2771–2784

    Article  Google Scholar 

  • Cressie N (1993) Statistics for Spatial data. Wiley, New York, NY

    Book  Google Scholar 

  • Eidsvik J, Shaby BA, Reich BJ, Wheeler M, Niemi J (2014) Estimation and prediction in spatial models with block composite likelihoods. J Comput Gr Stat 23:295–315

    Article  MathSciNet  Google Scholar 

  • Finley AO, Datta A, Cook BD, Morton DC, Andersen HE, Banerjee S (2019) Efficient algorithms for Bayesian nearest neighbor Gaussian processes. J Comput Gr Stat 28:401–414

    Article  MathSciNet  Google Scholar 

  • Fuglstad G-A, Simpson D, Lindgren F, Rue H (2015) Does non-stationary spatial data always require non-stationary random fields? Sp Stat 14:505–531

    Article  MathSciNet  Google Scholar 

  • Gneiting T, Raftery AE (2007) Strictly proper scoring rules, prediction, and estimation. J Am Stat Assoc 102:359–378

    Article  MathSciNet  Google Scholar 

  • Heaton MJ, Datta A, Finley AO, Furrer R, Guinness J, Guhaniyogi R, Gerber F, Gramacy RB, Hammerling D, Katzfuss M, Lindgren F, Nychka DW, Sun F, Zammit-Mangion A (2019) A case study competition among methods for analyzing large spatial data. J Agri, Biol Environ Stat 24:398–425

    Article  MathSciNet  Google Scholar 

  • Huang H, Abdulah S, Sun Y, Ltaief H, Keyes D. E., Genton M. G., (2021). Competition on spatial statistics for large datasets. J Agri, Biol Environ Stat. https://doi.org/10.1007/s13253-021-00457-z

  • Huang H, Blake L. R., Hammerling D. M., (2019). Pushing the limit: A hybrid parallel implementation of the multi-resolution approximation for massive data. arXiv:preprint,1905.00141

  • Nychka D, Bandyopadhyay S, Hammerling D, Lindgren F, Sain S (2015) A multiresolution Gaussian process model for the analysis of large spatial datasets. J Comput Graph Stat 24:579–599

    Article  MathSciNet  Google Scholar 

  • Pebesma EJ (2004) Multivariable geostatistics in S: the gstat package. Comput Geosci 30:683–691

    Article  Google Scholar 

  • R Core Team (2020) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria

  • Wikle C. K., Cressie N, Zammit-Mangion A, Shumack C, (2017). A Common Task Framework (CTF) for objective comparison of spatial prediction methodologies. In: Statistics Views, Wiley, Chichester, UK. https://www.statisticsviews.com/article/a-common-task-framework-ctf-for-objective-comparison-of-spatial-prediction-methodologies/

  • Xu G, Genton MG (2017) Tukey \(g\)-and-\(h\) random fields. J Am Stat Assoc 112:1236–1249

    Article  MathSciNet  Google Scholar 

  • Zammit-Mangion A, Cressie N (2021) FRK: An R package for spatial and spatio-temporal prediction with large datasets. J Stat Softw 98(4):1–48

    Article  Google Scholar 

  • Zammit-Mangion A, Ng TLJ, Vu Q, Filippone M (2021) Deep compositional spatial models. J Am Stat Assoc. https://doi.org/10.1080/01621459.2021.1887741

Download references

Acknowledgements

Q.V. and J.J. were each supported by a University Postgraduate Award from the University of Wollongong, Australia. A.R.P. and A.Z.-M. were supported by the Australian Research Council (ARC) Discovery Project DP190100180. A.Z.-M. was also supported by the ARC Discovery Early Career Research Award DE180100203.

Author information

Authors and Affiliations

  1. School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, Australia

    Quan Vu, Yi Cao, Josh Jacobson, Alan R. Pearse & Andrew Zammit-Mangion

Authors
  1. Quan Vu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yi Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Josh Jacobson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alan R. Pearse
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andrew Zammit-Mangion
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Quan Vu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vu, Q., Cao, Y., Jacobson, J. et al. Discussion on “Competition on Spatial Statistics for Large Datasets”. JABES 26, 614–618 (2021). https://doi.org/10.1007/s13253-021-00464-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13253-021-00464-0

Search

Navigation