Regression analysis of elliptically symmetric directional data
基于角高斯分布，开发了一套方向数据回归分析工具，包括检验旋转对称性、参数依赖协变量的方法，以及最小体积预测区域，并通过模拟和水文、生物信息学数据验证其有效性。
A comprehensive toolkit is developed for regression analysis of directional data based on a flexible class of angular Gaussian distributions. Informative testing procedures to assess rotational symmetry around the mean direction, and the dependence of model parameters on covariates are proposed. Bootstrap-based algorithms are provided to assess the significance of the proposed test statistics. Moreover, a prediction region that achieves the smallest volume in a class of ellipsoidal prediction regions of the same coverage probability is constructed. The efficacy of these inference procedures is demonstrated in simulation experiments. Finally, this new toolkit is used to analyze directional data originating from a hydrology study and a bioinformatics application.