Bootstrapping an inhomogeneous point process |
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Authors: | Ji Meng Loh |
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Institution: | Department of Statistics, Columbia University, New York, NY 10027, USA |
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Abstract: | In this paper, we focus on resampling non-stationary weakly dependent point processes in two dimensions to make inference on the inhomogeneous K function ( Baddeley et al., 2000). We provide theoretical results that show a consistency result of the bootstrap estimates of the variance as the observation region and resampling blocks increase in size. We present results of a simulation study that examines the performance of nominal 95% confidence intervals for the inhomogeneous K function obtained via our bootstrap procedure. The procedure is also applied to a rainforest dataset. |
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Keywords: | Inhomogeneous point process Inhomogeneous K function Marked point bootstrap Spatial bootstrap |
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