Abstract
Artificial neural networks(ANN) have been found to be an effective nonparametric method in many predictive applications. However, they have not been discussed much in the literature for angular data. In this article, we present two separate ANN models for angular-angular and linear-angular regression. We compare the performance of these ANN models with other regression models used in these contexts available in the literature. We find that the presented ANN models perform competitively and sometimes better as a predictive tool. We also propose two new methods for generating prediction intervals for ANN models. We find these prediction intervals provide good coverage probabilities on the Test dataset.
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Laha, A.K., Majumdar, S. (2022). Angular-Angular and Linear-Angular Regression Using ANN. In: SenGupta, A., Arnold, B.C. (eds) Directional Statistics for Innovative Applications. Forum for Interdisciplinary Mathematics. Springer, Singapore. https://doi.org/10.1007/978-981-19-1044-9_24
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DOI: https://doi.org/10.1007/978-981-19-1044-9_24
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