Alonso Pena, MaríaAmeijeiras Alonso, JoséCrujeiras Casais, Rosa María2025-10-272025-10-272020-09-100094-9655https://hdl.handle.net/10347/43421This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Statistical Computation and Simulation on 10 Sep 2020, available at: https://doi.org/10.1080/00949655.2020.1818243No matter the nature of the response and/or explanatory variables in a regression model, some basic issues such as the existence of an effect of the predictor on the response, or the assessment of a common shape across groups of observations, must be solved prior to model fitting. This is also the case for regression models involving circular variables (supported on the unit circumference). In that context, using kernel regression methods, this paper provides a flexible alternative for constructing pilot estimators that allow to construct suitable statistics to perform no-effect tests and tests for equality and parallelism of regression curves. Finite sample performance of the proposed methods is analysed in a simulation study and illustrated with real data examples.engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Nonparametric regressionAnalysis of covarianceBootstrapCircular predictorsCircular responses120906 Métodos de distribución libre y no paramétricajournal article10.1080/00949655.2020.18182431563-5163open access