Efficient Estimation of Partially Linear Models for Spatial Data over Complex Domain

TL;DR

This paper introduces a new efficient method for estimating partially linear models for spatial data on complex domains using bivariate splines, simplifying implementation and enabling statistical inference.

Contribution

It proposes a novel spline-based estimation approach that avoids finite element construction, simplifies implementation, and provides asymptotic properties and inference tools.

Findings

Method performs well in simulations.

Estimates are asymptotically normal.

Real data analysis confirms effectiveness.

Abstract

In this paper, we study the estimation of partially linear models for spatial data distributed over complex domains. We use bivariate splines over triangulations to represent the nonparametric component on an irregular two-dimensional domain. The proposed method is formulated as a constrained minimization problem which does not require constructing finite elements or locally supported basis functions. Thus, it allows an easier implementation of piecewise polynomial representations of various degrees and various smoothness over an arbitrary triangulation. Moreover, the constrained minimization problem is converted into an unconstrained minimization via a QR decomposition of the smoothness constraints, which allows for the development of a fast and efficient penalized least squares algorithm to fit the model. The estimators of the parameters are proved to be asymptotically normal under some regularity conditions. The estimator of the bivariate function is consistent, and its rate of convergence is also established. The proposed method enables us to construct confidence intervals and permits inference for the parameters. The performance of the estimators is evaluated by two simulation examples and by a real data analysis.