Skip to main content
Log in

Non-parametric adaptive bandwidth selection for kernel estimators of spatial intensity functions

  • Published:
Annals of the Institute of Statistical Mathematics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. www.knmi.nl/kennis-en-datacentrum/dataset/aardbevingscatalogus.

References

  • Abramson, I. A. (1982). On bandwidth variation in kernel estimates—A square root law. The Annals of Statistics, 10, 1217–1223.

    Article  MathSciNet  Google Scholar 

  • Baddeley, A., Rubak, E., Turner, R. (2015). Spatial point patterns: Methodology and applications with R. Boca Raton: CRC Press.

    Book  Google Scholar 

  • Barr, C. D., Schoenberg, F. P. (2010). On the Voronoi estimator for the intensity of an inhomogeneous planar Poisson process. Biometrika, 97, 977–984.

    Article  MathSciNet  Google Scholar 

  • 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.

    MathSciNet  Google Scholar 

  • 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.

    Article  MathSciNet  Google Scholar 

  • 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.

    Article  MathSciNet  Google Scholar 

  • Chacón, J. E., Duong, T. (2018). Kernel smoothing and its applications. Boca Raton: CRC Press.

    Google Scholar 

  • Chiu, S. N., Stoyan, D., Kendall, W. S., Mecke, J. (2013). Stochastic geometry and its applications, 3rd ed., Chichester: Wiley.

    Book  Google Scholar 

  • 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.

    Article  MathSciNet  Google Scholar 

  • Davies, T. M., Baddeley, A. (2018). Fast computation of spatially adaptive kernel estimates. Statistics and Computing, 28, 937–956.

    Article  MathSciNet  Google Scholar 

  • 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.

    Article  MathSciNet  Google Scholar 

  • Diggle, P. J. (1985). A kernel method for smoothing point process data. Applied Statistics, 34, 138–147.

    Article  Google Scholar 

  • Diggle, P. J. (2014). Statistical analysis of spatial and spatio-temporal point patterns, 3rd ed., Boca Raton: CRC Press.

    Google Scholar 

  • Du Rietz, G. E. (1929). The fundamental units of vegetation. Proceedings of the International Congress of Plant Science, 1, 623–627.

    Google Scholar 

  • Hall, P., Hu, T. C., Marron, J. S. (1995). Improved variable window kernel estimates of probability densities. The Annals of Statistics, 23, 1–10.

    Article  MathSciNet  Google Scholar 

  • Hall, P., Minnotte, M. C., Zhang, C. (2004). Bump hunting with non-Gaussian kernels. The Annals of Statistics, 32, 2124–2141.

    Article  MathSciNet  Google Scholar 

  • Illian, J., Penttinen, A., Stoyan, H., Stoyan, D. (2008). Statistical analysis and modelling of spatial point patterns. Chichester: Wiley.

    Google Scholar 

  • 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.

    Article  MathSciNet  Google Scholar 

  • van Lieshout, M. N. M. (2019). Theory of spatial statistics: A concise introduction. Boca Raton: CRC Press.

    Book  Google Scholar 

  • 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.

    Article  MathSciNet  Google Scholar 

  • 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.

    Article  MathSciNet  Google Scholar 

  • 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.

    Book  Google Scholar 

  • Matérn, B. (1986). Spatial variation. Berlin: Springer.

    Book  Google Scholar 

  • 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.

    Article  MathSciNet  Google Scholar 

  • Ord, J. K. (1978). How many trees in a forest? Mathematical Sciences, 3, 23–33.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    ADS  Google Scholar 

  • Scott, D. W. (1992). Multivariate density estimation: Theory, practice and visualization. New York: Wiley.

    Book  Google Scholar 

  • Silverman, B. W. (1986). Density estimation for statistics and data analysis. London: Chapman & Hall.

    Google Scholar 

  • Stoyan, D., Grabarnik, P. (1991). Second-order characteristics for stochastic structures connected with Gibbs point processes. Mathematische Nachrichten, 151, 95–100.

    Article  MathSciNet  Google Scholar 

  • Wand, M. P., Jones, M. C. (1994). Kernel smoothing. Boca Raton: Chapman & Hall.

    Book  Google Scholar 

Download references

Acknowledgements

Many thanks are due to the anonymous referees for thoughtful suggestions that helped to improve the manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. N. M. van Lieshout.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This research was supported by The Netherlands Organisation for Scientific Research NWO (project DEEP.NL.2018.033).

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10463-023-00890-6

Keywords

Navigation