Semi-parametric least-area linear-circular regression through M\"{o}bius transformation

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• Surojit Biswas (Indian Institute of Technology Kharagpur)
• Wednesday 07 May 2025, 14:00-14:30
• Seminar Room 1, Newton Institute.

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RCLW01 - Uncertainty in multivariate, non-Euclidean, and functional spaces: theory and practice

This paper introduces a novel regression model designed for angular response variables with linear predictors, utilizing a generalized Mobius transformation to define the regression curve. By mapping the real axis to the circle, the model effectively captures the relationship between linear and angular components. A key innovation is the introduction of an area-based loss function, inspired by the geometry of a curved torus, for efficient parameter estimation. The semi-parametric nature of the model eliminates the need for specific distributional assumptions about the angular error, enhancing its versatility. Extensive simulation studies, incorporating von Mises and wrapped Cauchy distributions, highlight the robustness of the framework. The model’s practical utility is demonstrated through real-world data analysis of Bitcoin and Ethereum, showcasing its ability to derive meaningful insights from complex data structures.

This talk is part of the Isaac Newton Institute Seminar Series series.

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