Abstract
Analyzing the escape direction of animals subject to covariates is a problem that requires statistical techniques beyond classical regression methods. Apart from the periodicity of the angle of direction, which demands the use of circular statistics, animal escape data usually call for the exploration of the preferred orientations rather than the expected orientation. In this paper we propose the use of a nonparametric method to estimate the conditional local modes of the escape directions of animals from a regression perspective. We present the estimation algorithms and study the asymptotic properties of the estimators as well as its finite sample performance through some simulation experiments. Our proposal is used to model the escape behavior of a group of larval zebrafish escaping from a robot predator. More broadly, the approach presented in this paper can be applied to many existing problems related to animal behavior or other fields.
Funding Statement
This work was supported by grant PID2020-116587GB-I00 funded by MCIN/ AEI/10.13039/501100011033 and the Competitive Reference Groups 2021/2024 (ED431C 2021/24) from the Xunta de Galicia through the ERDF. Research of M. Alonso-Pena was supported by the Xunta de Galicia through the grant ED481A-2019/139 from the Consellería de Educación, Universidade e Formación Profesional.
Acknowledgments
The authors thank the Editor, Associate Editor and the anonymous reviewers for their helpful comments, which considerably improved the quality of the manuscript. The authors also acknowledge the Supercomputing Center of Galicia (CESGA) for the computational resources.
Citation
María Alonso-Pena. Rosa M. Crujeiras. "Analyzing animal escape data with circular nonparametric multimodal regression." Ann. Appl. Stat. 17 (1) 130 - 152, March 2023. https://doi.org/10.1214/22-AOAS1619
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