CircSiZer: an exploratory tool for circular data

TL;DR

CircSiZer is a new graphical tool extending SiZer for circular data, enabling the exploration of significant features in circular density and regression analysis, demonstrated on wind data and simulations.

Contribution

It introduces CircSiZer, a novel extension of SiZer for circular data, with a graphical interface for significance assessment in density and regression analysis.

Findings

Effective in analyzing wind direction data

Successfully identifies significant features in simulated data

Provides a visual assessment of data structures

Abstract

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.