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

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  • Tonglin Zhang
  • Ge Lin

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  • 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
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2008.01069.x
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    References listed on IDEAS

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    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.
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    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.

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