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Global envelope tests for spatial processes

Author

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  • Mari Myllymäki
  • Tomáš Mrkvička
  • Pavel Grabarnik
  • Henri Seijo
  • Ute Hahn

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Suggested Citation

  • Mari Myllymäki & Tomáš Mrkvička & Pavel Grabarnik & Henri Seijo & Ute Hahn, 2017. "Global envelope tests for spatial processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 381-404, March.
    • Handle: RePEc:bla:jorssb:v:79:y:2017:i:2:p:381-404
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    File URL: http://hdl.handle.net/10.1111/rssb.12172
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    References listed on IDEAS

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    1. Kasper K. Berthelsen & Jesper Møller, 2003. "Likelihood and Non‐parametric Bayesian MCMC Inference for Spatial Point Processes Based on Perfect Simulation and Path Sampling," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 30(3), pages 549-564, September.
    2. Dufour, Jean-Marie, 2006. "Monte Carlo tests with nuisance parameters: A general approach to finite-sample inference and nonstandard asymptotics," Journal of Econometrics, Elsevier, vol. 133(2), pages 443-477, August.
    3. Grabarnik, Pavel & Myllymäki, Mari & Stoyan, Dietrich, 2011. "Correct testing of mark independence for marked point patterns," Ecological Modelling, Elsevier, vol. 222(23), pages 3888-3894.
    4. Baddeley, Adrian & Turner, Rolf, 2005. "spatstat: An R Package for Analyzing Spatial Point Patterns," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i06).
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Jiří Dvořák & Tomáš Mrkvička & Jorge Mateu & Jonatan A. González, 2022. "Nonparametric Testing of the Dependence Structure Among Points–Marks–Covariates in Spatial Point Patterns," International Statistical Review, International Statistical Institute, vol. 90(3), pages 592-621, December.
    2. Chaiban, Celia & Biscio, Christophe & Thanapongtharm, Weerapong & Tildesley, Michael & Xiao, Xiangming & Robinson, Timothy P. & Vanwambeke, Sophie O. & Gilbert, Marius, 2019. "Point pattern simulation modelling of extensive and intensive chicken farming in Thailand: Accounting for clustering and landscape characteristics," Agricultural Systems, Elsevier, vol. 173(C), pages 335-344.
    3. Jakob G. Rasmussen & Heidi S. Christensen, 2021. "Point Processes on Directed Linear Networks," Methodology and Computing in Applied Probability, Springer, vol. 23(2), pages 647-667, June.
    4. Dai, Wenlin & Genton, Marc G., 2019. "Directional outlyingness for multivariate functional data," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 50-65.
    5. Adil Yazigi & Antti Penttinen & Anna-Kaisa Ylitalo & Matti Maltamo & Petteri Packalen & Lauri Mehtätalo, 2022. "Modeling Forest Tree Data Using Sequential Spatial Point Processes," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(1), pages 88-108, March.
    6. Kateřina Koňasová & Jiří Dvořák, 2021. "Stochastic Reconstruction for Inhomogeneous Point Patterns," Methodology and Computing in Applied Probability, Springer, vol. 23(2), pages 527-547, June.
    7. Dai, Wenlin & Mrkvička, Tomáš & Sun, Ying & Genton, Marc G., 2020. "Functional outlier detection and taxonomy by sequential transformations," Computational Statistics & Data Analysis, Elsevier, vol. 149(C).
    8. Myllymäki, Mari & Kuronen, Mikko & Bianchi, Simone & Pommerening, Arne & Mehtätalo, Lauri, 2024. "A Bayesian approach to projecting forest dynamics and related uncertainty: An application to continuous cover forests," Ecological Modelling, Elsevier, vol. 491(C).
    9. Jesper Møller & Heidi S. Christensen & Francisco Cuevas-Pacheco & Andreas D. Christoffersen, 2021. "Structured Space-Sphere Point Processes and K-Functions," Methodology and Computing in Applied Probability, Springer, vol. 23(2), pages 569-591, June.
    10. Ghorbani, Mohammad & Vafaei, Nafiseh & Dvořák, Jiří & Myllymäki, Mari, 2021. "Testing the first-order separability hypothesis for spatio-temporal point patterns," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    11. Ninna Vihrs & Jesper Møller & Alan E. Gelfand, 2022. "Approximate Bayesian inference for a spatial point process model exhibiting regularity and random aggregation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 185-210, March.
    12. Diquigiovanni, Jacopo & Fontana, Matteo & Vantini, Simone, 2022. "Conformal prediction bands for multivariate functional data," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    13. Vesna Gotovac Dogaš & Kateřina Helisová, 2021. "Testing Equality of Distributions of Random Convex Compact Sets via Theory of 𝕹 $\mathfrak {N}$ -Distances," Methodology and Computing in Applied Probability, Springer, vol. 23(2), pages 503-526, June.
    14. Tomáš Mrkvička & Tomáš Roskovec & Michael Rost, 2021. "A Nonparametric Graphical Tests of Significance in Functional GLM," Methodology and Computing in Applied Probability, Springer, vol. 23(2), pages 593-612, June.
    15. Johan Debayle & Vesna Gotovac Ðogaš & Kateřina Helisová & Jakub Staněk & Markéta Zikmundová, 2021. "Assessing Similarity of Random sets via Skeletons," Methodology and Computing in Applied Probability, Springer, vol. 23(2), pages 471-490, June.
    16. Johannes Wieditz & Yvo Pokern & Dominic Schuhmacher & Stephan Huckemann, 2022. "Characteristic and necessary minutiae in fingerprints," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(1), pages 27-50, January.
    17. José Ulises Márquez Urbina & Graciela González Farías & L Leticia Ramírez Ramírez & D Iván Rodríguez González, 2022. "A multi-source global-local model for epidemic management," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-26, January.
    18. Veronika Římalová & Alessandra Menafoglio & Alessia Pini & Vilém Pechanec & Eva Fišerová, 2020. "A permutation approach to the analysis of spatiotemporal geochemical data in the presence of heteroscedasticity," Environmetrics, John Wiley & Sons, Ltd., vol. 31(4), June.
    19. Jiří Dvořák & Tomáš Mrkvička, 2022. "Graphical tests of independence for general distributions," Computational Statistics, Springer, vol. 37(2), pages 671-699, April.
    20. Jesper Møller & Ninna Vihrs, 2022. "Should We Condition on the Number of Points When Modelling Spatial Point Patterns?," International Statistical Review, International Statistical Institute, vol. 90(3), pages 551-562, December.

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