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Fast block variance estimation procedures for inhomogeneous spatial point processes

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  • Yongtao Guan

Abstract

We introduce two new variance estimation procedures that use non-overlapping and overlapping blocks, respectively. The non-overlapping blocks estimator can be viewed as the limit of the thinned block bootstrap estimator recently proposed in Guan Loh (2007), by letting the number of thinned processes and bootstrap samples therein both increase to infinity. The non-overlapping blocks estimator can be obtained quickly since it does not require any thinning or bootstrap steps, and it is more stable. The overlapping blocks estimator further improves the performance of the non-overlapping blocks with a modest increase in computation time. A simulation study demonstrates the superiority of the proposed estimators over the thinned block bootstrap estimator. Copyright 2009, Oxford University Press.

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  • Yongtao Guan, 2009. "Fast block variance estimation procedures for inhomogeneous spatial point processes," Biometrika, Biometrika Trust, vol. 96(1), pages 213-220.
    • Handle: RePEc:oup:biomet:v:96:y:2009:i:1:p:213-220
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    File URL: http://hdl.handle.net/10.1093/biomet/asn072
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    Cited by:

    1. Yongtao Guan & Jun Yan & Rajita Sinha, 2011. "Variance Estimation for Statistics Computed from Single Recurrent Event Processes," Biometrics, The International Biometric Society, vol. 67(3), pages 711-718, September.
    2. Yu Ryan Yue & Ji Meng Loh, 2011. "Bayesian Semiparametric Intensity Estimation for Inhomogeneous Spatial Point Processes," Biometrics, The International Biometric Society, vol. 67(3), pages 937-946, September.
    3. Ute Hahn & Eva B. Vedel Jensen, 2016. "Hidden Second-order Stationary Spatial Point Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 455-475, June.

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