CircSiZer: an exploratory tool for circular data. Smoothing methods and SiZer (SIgnificant ZERo crossing of the derivatives) are useful tools for exploring significant underlying structures in data samples. An extension of SiZer to circular data, namely CircSiZer, is introduced. Based on scale-space ideas, CircSiZer presents a graphical device to assess which observed features are statistically significant, both for density and regression analysis with circular data. The method is intended for analyzing the behavior of wind direction in the atlantic coast of Galicia (NW Spain) and how it has an influence over wind speed. The performance of CircSiZer is also checked with some simulated examples.
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References in zbMATH (referenced in 8 articles )
Showing results 1 to 8 of 8.
- Crujeiras, Rosa M.; Saavedra-Nieves, Paula: Comments on: “Recent advances in directional statistics” (2021)
- Pewsey, Arthur; García-Portugués, Eduardo: Recent advances in directional statistics (2021)
- Giovanna Jona Lasinio; Gianluca Mastrantonio; Mario Santoro: CircSpaceTime: an R package for spatial and spatio-temporal modeling of Circular data (2020) arXiv
- Lasinio, Giovanna Jona; Santoro, Mario; Mastrantonio, Gianluca: CircSpaceTime: an R package for spatial and spatio-temporal modelling of circular data (2020)
- Jose Ameijeiras-Alonso, Rosa M. Crujeiras, Alberto Rodríguez-Casal: Multimode: An R Package for Mode Assessment (2018) arXiv
- Vuollo, Ville; Holmström, Lasse: A scale space approach for exploring structure in spherical data (2018)
- Huckemann, Stephan; Kim, Kwang-Rae; Munk, Axel; Rehfeldt, Florian; Sommerfeld, Max; Weickert, Joachim; Wollnik, Carina: The circular SiZer, inferred persistence of shape parameters and application to early stem cell differentiation (2016)
- María Oliveira; Rosa Crujeiras; Alberto Rodríguez-Casal: NPCirc: An R Package for Nonparametric Circular Methods (2014) not zbMATH