Abstract
We introduce a new fully non-parametric two-step adaptive bandwidth selection method for kernel estimators of spatial point process intensity functions based on the Campbell–Mecke formula and Abramson’s square root law. We present a simulation study to assess its performance relative to other adaptive and global bandwidth selectors, investigate the influence of the pilot estimator and apply the technique to two data sets: A pattern of trees and an earthquake catalogue.
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References
Abramson, I. A. (1982). On bandwidth variation in kernel estimates—A square root law. The Annals of Statistics, 10, 1217–1223.
Baddeley, A., Rubak, E., Turner, R. (2015). Spatial point patterns: Methodology and applications with R. Boca Raton: CRC Press.
Barr, C. D., Schoenberg, F. P. (2010). On the Voronoi estimator for the intensity of an inhomogeneous planar Poisson process. Biometrika, 97, 977–984.
Berman, M., Diggle, P. J. (1989). Estimating weighted integrals of the second-order intensity of a spatial point process. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 51, 81–92.
Bowman, A. W., Foster, P. J. (1993). Adaptive smoothing and density-based tests of multivariate normality. Journal of the American Statistical Association, 88, 529–537.
Brooks, M. M., Marron, J. S. (1991). Asymptotic optimality of the least-squares cross-validation bandwidth for kernel estimates of intensity functions. Stochastic Processes and their Applications, 38, 157–165.
Chacón, J. E., Duong, T. (2018). Kernel smoothing and its applications. Boca Raton: CRC Press.
Chiu, S. N., Stoyan, D., Kendall, W. S., Mecke, J. (2013). Stochastic geometry and its applications, 3rd ed., Chichester: Wiley.
Cronie, O., van Lieshout, M. N. M. (2018). A non-model based approach to bandwidth selection for kernel estimators of spatial intensity functions. Biometrika, 105, 455–462.
Davies, T. M., Baddeley, A. (2018). Fast computation of spatially adaptive kernel estimates. Statistics and Computing, 28, 937–956.
Davies, T. M., Flynn, C. R., Hazelton, M. L. (2018). On the utility of asymptotic bandwidth selectors for spatially adaptive kernel density estimation. Statistics and Probability Letters, 138, 75–81.
Diggle, P. J. (1985). A kernel method for smoothing point process data. Applied Statistics, 34, 138–147.
Diggle, P. J. (2014). Statistical analysis of spatial and spatio-temporal point patterns, 3rd ed., Boca Raton: CRC Press.
Du Rietz, G. E. (1929). The fundamental units of vegetation. Proceedings of the International Congress of Plant Science, 1, 623–627.
Hall, P., Hu, T. C., Marron, J. S. (1995). Improved variable window kernel estimates of probability densities. The Annals of Statistics, 23, 1–10.
Hall, P., Minnotte, M. C., Zhang, C. (2004). Bump hunting with non-Gaussian kernels. The Annals of Statistics, 32, 2124–2141.
Illian, J., Penttinen, A., Stoyan, H., Stoyan, D. (2008). Statistical analysis and modelling of spatial point patterns. Chichester: Wiley.
van Lieshout, M. N. M. (2012). On estimation of the intensity function of a point process. Methodology and Computing in Applied Probability, 14, 567–578.
van Lieshout, M. N. M. (2019). Theory of spatial statistics: A concise introduction. Boca Raton: CRC Press.
van Lieshout, M. N. M. (2020). Infill asymptotics and bandwidth selection for kernel estimators of spatial intensity functions. Methodology and Computing in Applied Probability, 22, 995–1008.
van Lieshout, M. N. M. (2021). Infill asymptotics for adaptive kernel estimators of spatial intensity functions. Australian and New Zealand Journal of Statistics, 63, 159–181.
Lo, P. H. (2017). An iterative plug-in algorithm for optimal bandwidth selection in kernel intensity estimation for spatial data, PhD Thesis, Technical University of Kaiserslautern.
Loader, C. (1999). Local regression and likelihood. New York: Springer.
Matérn, B. (1986). Spatial variation. Berlin: Springer.
Moradi, M. M., Cronie, O., Rubak, E., Lachièze-Rey, R., Mateu, J., Baddeley, A. (2019). Resample-smoothing of Voronoi intensity estimators. Statistics and Computing, 29, 995–1010.
Ord, J. K. (1978). How many trees in a forest? Mathematical Sciences, 3, 23–33.
Platt, W. J., Evans, G. W., Rathbun, S. L. (1988). The population dynamics of a long-lived Conifer (Pinus palustris). The American Naturalist, 131, 491–525.
Rathbun, S. L., Cressie, N. (1994). A space-time survival point process for a longleaf pine forest in southern Georgia. Journal of the American Statistical Association, 89, 1164–1173.
Schaap, W. E., van de Weygaert, R. (2000). Letter to the editor. Continuous fields and discrete samples: Reconstruction through Delaunay tessellations. Astronomy and Astrophysics, 363, L29–L32.
Scott, D. W. (1992). Multivariate density estimation: Theory, practice and visualization. New York: Wiley.
Silverman, B. W. (1986). Density estimation for statistics and data analysis. London: Chapman & Hall.
Stoyan, D., Grabarnik, P. (1991). Second-order characteristics for stochastic structures connected with Gibbs point processes. Mathematische Nachrichten, 151, 95–100.
Wand, M. P., Jones, M. C. (1994). Kernel smoothing. Boca Raton: Chapman & Hall.
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Many thanks are due to the anonymous referees for thoughtful suggestions that helped to improve the manuscript.
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This research was supported by The Netherlands Organisation for Scientific Research NWO (project DEEP.NL.2018.033).
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van Lieshout, M.N.M. Non-parametric adaptive bandwidth selection for kernel estimators of spatial intensity functions. Ann Inst Stat Math 76, 313–331 (2024). https://doi.org/10.1007/s10463-023-00890-6
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DOI: https://doi.org/10.1007/s10463-023-00890-6