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Estimating equations for spatially correlated data in multi-dimensional space

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  • Pei-Sheng Lin

Abstract

We use the quasilikelihood concept to propose an estimating equation for spatial data with correlation across the study region in a multi-dimensional space. With appropriate mixing conditions, we develop a central limit theorem for a random field under various L p metrics. The consistency and asymptotic normality of quasilikelihood estimators can then be derived. We also conduct simulations to evaluate the performance of the proposed estimating equation, and a dataset from East Lansing Woods is used to illustrate the method. Copyright 2008, Oxford University Press.

Suggested Citation

  • Pei-Sheng Lin, 2008. "Estimating equations for spatially correlated data in multi-dimensional space," Biometrika, Biometrika Trust, vol. 95(4), pages 847-858.
    • Handle: RePEc:oup:biomet:v:95:y:2008:i:4:p:847-858
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    File URL: http://hdl.handle.net/10.1093/biomet/asn046
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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. Shang, Zuofeng, 2012. "On latent process models in multi-dimensional space," Statistics & Probability Letters, Elsevier, vol. 82(7), pages 1259-1266.
    3. Lin, Pei-Sheng & Chen, Feng-Chi & Kuo, Shu-Fu & Kung, Yi-Hung, 2014. "Assessing the relationship of evolutionary rates and functional variables by mixture estimating equations," Statistics & Probability Letters, Elsevier, vol. 94(C), pages 248-256.

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