IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v65y2009i2p353-360.html
   My bibliography  Save this article

Cluster Detection Based on Spatial Associations and Iterated Residuals in Generalized Linear Mixed Models

Author

Listed:
  • Tonglin Zhang
  • Ge Lin

Abstract

No abstract is available for this item.

Suggested Citation

  • Tonglin Zhang & Ge Lin, 2009. "Cluster Detection Based on Spatial Associations and Iterated Residuals in Generalized Linear Mixed Models," Biometrics, The International Biometric Society, vol. 65(2), pages 353-360, June.
    • Handle: RePEc:bla:biomet:v:65:y:2009:i:2:p:353-360
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2008.01069.x
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. A. Baddeley & R. Turner & J. Møller & M. Hazelton, 2005. "Residual analysis for spatial point processes (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 617-666, November.
    2. Jiancang Zhuang, 2006. "Second‐order residual analysis of spatiotemporal point processes and applications in model evaluation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(4), pages 635-653, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Pei-Sheng Lin, 2014. "Generalized Scan Statistics for Disease Surveillance," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(3), pages 791-808, September.
    2. Ge Lin & Tonglin Zhang, 2024. "Spatial monitoring to reduce COVID-19 vaccine hesitance," Journal of Geographical Systems, Springer, vol. 26(2), pages 249-264, April.
    3. Pei‐Sheng Lin & Yi‐Hung Kung & Murray Clayton, 2016. "Spatial scan statistics for detection of multiple clusters with arbitrary shapes," Biometrics, The International Biometric Society, vol. 72(4), pages 1226-1234, December.
    4. Kunihiko Takahashi & Hideyasu Shimadzu, 2018. "Multiple-cluster detection test for purely temporal disease clustering: Integration of scan statistics and generalized linear models," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-15, November.
    5. Zhang, Tonglin & Lin, Ge, 2016. "On Moran’s I coefficient under heterogeneity," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 83-94.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Roba Bairakdar & Debbie Dupuis & Melina Mailhot, 2024. "Deviance Voronoi Residuals for Space-Time Point Process Models: An Application to Earthquake Insurance Risk," Papers 2410.04369, arXiv.org.
    2. A. Baddeley & J. Møller & A. Pakes, 2008. "Properties of residuals for spatial point processes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(3), pages 627-649, September.
    3. Nicoletta D’Angelo & Marianna Siino & Antonino D’Alessandro & Giada Adelfio, 2022. "Local spatial log-Gaussian Cox processes for seismic data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(4), pages 633-671, December.
    4. Yongtao Guan, 2008. "Variance estimation for statistics computed from inhomogeneous spatial point processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 175-190, February.
    5. Janine B. Illian & David F. R. P. Burslem, 2017. "Improving the usability of spatial point process methodology: an interdisciplinary dialogue between statistics and ecology," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(4), pages 495-520, October.
    6. Heinrich Lothar & Klein Stella, 2011. "Central limit theorem for the integrated squared error of the empirical second-order product density and goodness-of-fit tests for stationary point processes," Statistics & Risk Modeling, De Gruyter, vol. 28(4), pages 359-387, December.
    7. Guangshun Bai & Xuemei Yang & Guangxin Bai & Zhigang Kong & Jieyong Zhu & Shitao Zhang, 2024. "Examining the Controls on the Spatial Distribution of Landslides Triggered by the 2008 Wenchuan Ms 8.0 Earthquake, China, Using Methods of Spatial Point Pattern Analysis," Sustainability, MDPI, vol. 16(16), pages 1-24, August.
    8. Giada Adelfio & Marcello Chiodi, 2021. "Including covariates in a space-time point process with application to seismicity," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 947-971, September.
    9. Xin Chen & Jieli Ding & Liuquan Sun, 2018. "A semiparametric additive rate model for a modulated renewal process," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(4), pages 675-698, October.
    10. Dereudre, D. & Lavancier, F., 2011. "Practical simulation and estimation for Gibbs Delaunay-Voronoi tessellations with geometric hardcore interaction," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 498-519, January.
    11. Yehua Li & Yongtao Guan, 2014. "Functional Principal Component Analysis of Spatiotemporal Point Processes With Applications in Disease Surveillance," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1205-1215, September.
    12. Fengchang Lin & Jason P. Fine, 2009. "Pseudomartingale estimating equations for modulated renewal process models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 3-23, January.
    13. Ian W. Renner & David I. Warton, 2013. "Equivalence of MAXENT and Poisson Point Process Models for Species Distribution Modeling in Ecology," Biometrics, The International Biometric Society, vol. 69(1), pages 274-281, March.
    14. Jean-François Coeurjolly & Ege Rubak, 2013. "Fast Covariance Estimation for Innovations Computed from a Spatial Gibbs Point Process," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 669-684, December.
    15. D'Angelo, Nicoletta & Adelfio, Giada & Mateu, Jorge, 2023. "Locally weighted minimum contrast estimation for spatio-temporal log-Gaussian Cox processes," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    16. Jesper Møller & Carlos Díaz‐Avalos, 2010. "Structured Spatio‐Temporal Shot‐Noise Cox Point Process Models, with a View to Modelling Forest Fires," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(1), pages 2-25, March.
    17. Andrew J Edelman, 2012. "Positive Interactions between Desert Granivores: Localized Facilitation of Harvester Ants by Kangaroo Rats," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-9, February.
    18. Amanda S. Hering & Sean Bair, 2014. "Characterizing spatial and chronological target selection of serial offenders," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(1), pages 123-140, January.
    19. Ottmar Cronie & Mehdi Moradi & Christophe A N Biscio, 2024. "A cross-validation-based statistical theory for point processes," Biometrika, Biometrika Trust, vol. 111(2), pages 625-641.
    20. Yongtao Guan & Hansheng Wang, 2010. "Sufficient dimension reduction for spatial point processes directed by Gaussian random fields," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 367-387, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:biomet:v:65:y:2009:i:2:p:353-360. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.