Gijbels, IreneAlonso Pena, MaríaCrujeiras Casais, Rosa María2025-10-272025-10-272023-12-21Alonso-Pena, M., Gijbels, I., Crujeiras, R. M. (2023). A General Framework for Circular Local Likelihood Regression. "Journal of the American Statistical Association", vol. 119, 548, 2709–2721.0162-1459https://hdl.handle.net/10347/43414This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the American Statistical Association on 21 Dec 2023, available at: https://doi.org/10.1080/01621459.2023.2272786This paper presents a general framework for the estimation of regression models with circular covariates, where the conditional distribution of the response given the covariate can be specified through a parametric model. The estimation of a conditional characteristic is carried out nonparametrically, by maximizing the circular local likelihood, and the estimator is shown to be asymptotically normal. The problem of selecting the smoothing parameter is also addressed, as well as bias and variance computation. The performance of the estimation method in practice is studied through an extensive simulation study, where we cover the cases of Gaussian, Bernoulli, Poisson and Gamma distributed responses. The generality of our approach is illustrated with several real-data examples from different fields.eng© 2023 Alonso-Pena, M., Gijbels, I. and Crujeiras, R.M. Journal of the American Statistical Association published by Taylor & Francis. It may be used for non-commercial purposes in accordance with the publisher’s policy.Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Circular dataData-driven smoothing selectionLocal likelihoodNonparametric regression120906 Métodos de distribución libre y no paramétricajournal article10.1080/01621459.2023.22727861537-274Xopen access