Abstract
Kernel estimation is a popular approach to estimation of the pair correlation function which is a fundamental spatial point process characteristic. Least squares cross validation was suggested by Guan [A least-squares cross-validation bandwidth selection approach in pair correlation function estimations. Statist Probab Lett. 2007;77(18):1722–1729] as a data-driven approach to select the kernel bandwidth. The method can, however, be computationally demanding for large point pattern data sets. We suggest a modified least squares cross validation approach that is asymptotically equivalent to the one proposed by Guan but is computationally much faster.
Originalsprog | Engelsk |
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Tidsskrift | Journal of Statistical Computation and Simulation |
Vol/bind | 88 |
Udgave nummer | 10 |
Sider (fra-til) | 2001-2011 |
Antal sider | 11 |
ISSN | 0094-9655 |
DOI | |
Status | Udgivet - 3 jul. 2018 |
Begivenhed | 2nd Latin American Conference on Statistical Computing - Valparaiso, Chile Varighed: 9 mar. 2017 → 11 mar. 2017 |
Konference
Konference | 2nd Latin American Conference on Statistical Computing |
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Land/Område | Chile |
By | Valparaiso |
Periode | 09/03/2017 → 11/03/2017 |