This 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 the conditional mean, or a transformation of it, is carried out nonparametrically, by maximizing the circular local likelihood. 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.

翻译：本文提出了用循环共变法估计回归模型的一般框架,其中通过参数模型可以具体确定根据共变法作出的答复的有条件分布;通过尽量扩大循环当地可能性,以非对称方式估计有条件平均值或其变异;还解决了选择平滑参数的问题,以及偏差和差异计算;通过广泛的模拟研究研究,研究估算方法的实际表现,我们研究了高山、伯努利、普瓦松和伽马等分布式答复的案例;用几个真实数据实例说明了我们方法的一般性。