Spatial regression models over two-dimensional manifolds
提出一种适用于一般二维黎曼流形上空间数据的回归模型，通过共形参数化和有限元方法估计，可纳入空间变化的协变量信息。
We propose a regression model for data spatially distributed over general two-dimensional Riemannian manifolds. This is a generalized additive model with a roughness penalty term involving a differential operator computed over the non-planar domain. By virtue of a semiparametric framework, the model allows inclusion of space-varying covariate information. Estimation can be performed by conformally parameterizing the non-planar domain and then generalizing existing models for penalized spatial regression over planar domains. The conformal coordinates and the estimation problem are both computed with a finite element approach.