论文标题
循环局部可能性回归的一般框架
A general framework for circular local likelihood regression
论文作者
论文摘要
本文提出了一个通用框架，用于用圆形协变量估算回归模型，其中可以通过参数模型指定给定协变量的响应的条件分布。条件特征的估计是通过最大化循环局部可能性进行非参数进行的，并且估计量在渐近上是渐近正常的。还解决了选择平滑参数的问题，以及偏差和方差计算。实践中的估计方法的性能通过广泛的模拟研究进行了研究，在该研究中，我们涵盖了高斯，伯诺利，泊松和伽马的案例。通过来自不同领域的几个真实数据示例来说明我们方法的通用性。
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 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.
