Intensity estimation on geometric networks with penalized splines

• Marc Schneble ^[1] ; Göran Kauermann
  1. [1] LMU Munich
• Localización: Proceedings of the 35th International Workshop on Statistical Modelling : July 20-24, 2020 Bilbao, Basque Country, Spain / Itziar Irigoien Garbizu (ed. lit.), Dae-Jin Lee (ed. lit.), Joaquín Martínez Minaya (ed. lit.), María Xosé Rodríguez Álvarez (ed. lit.), 2020, ISBN 978-84-1319-267-3, págs. 204-209
• Idioma: inglés
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  • In this article we consider so called geometric networks. Typical examples are road networks or other infrastructure networks. We observe network based point processes and our task is to estimate the intensity (or density) of the processes. Available routines that tackle this problem are commonly based on kernel smoothing methods. However, kernel based estimation in general exhibits some drawbacks such as su ering from boundary e ects and the locality of the smoother. In an Euclidean space, the disadvantages of kernel methods can be overcome by using penalized spline smoothing. We here extend penalized spline smoothing towards smooth intensity estimation on geometric networks and apply the approach to both, simulated and real world data. The results show that penalized spline based intensity estimation outperforms kernel based methods.