Series ridge regression for spatial data on $\mathbb{R}^d$

This paper develops a general asymptotic theory of series ridge estimators for spatial data observed at irregularly spaced locations in a sampling region $R_n \subset \mathbb{R}^d$. We adopt a stochastic sampling design that can generate irregularly spaced sampling sites in a flexible manner including both pure increasing and mixed increasing domain frameworks. Specifically, we consider a spatial trend regression model and a nonparametric regression model with spatially dependent covariates. For these models, we investigate the $L^2$-penalized series estimation of the trend and regression functions and establish (i) uniform and $L^2$ convergence rates and (ii) multivariate central limit theorems for general series estimators, (iii) optimal uniform and $L^2$ convergence rates for spline and wavelet series estimators, and (iv) show that our dependence structure conditions on the underlying spatial processes cover a wide class of random fields including L\'evy-driven continuous autoregressive and moving average random fields.