Projected Multivariate Linear Models for Directional Data
提出一种球面投影多元线性模型，将方向观测视为多元线性模型未观测响应的单位球面投影，针对圆形数据可用迭代法快速计算最大似然估计，优于现有方法。
Abstract We introduce the spherically projected multivariate linear model for directional data. This model treats directional observations as projections onto the unit sphere of unobserved responses from a multivariate linear model. Focusing on the important case of circular data, we show that maximum likelihood estimates for the model are readily computed using iterative methods, in sharp contrast with competing approaches. Examples are given to demonstrate the resulting methodology in realistic applications.