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CircSiZer: an exploratory tool for circular data
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AI
DOI：10.1007/s10651-013-0249-0.png)
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Abstract
En 中文
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.
Keywords:
Circular data
CircSiZer
Nonparametric estimation
Wind pattern
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