A new class of functional conditional autoregressive models

TL;DR

This paper introduces a novel class of functional conditional autoregressive models for spatially dependent data, providing consistent and superconsistent estimators with theoretical guarantees and practical applications.

Contribution

It develops a new estimation framework for spatial functional data, establishing consistency and superconsistency of estimators, and demonstrates its effectiveness through simulations and real data analysis.

Findings

Covariance estimator is consistent under the proposed method.

Spatial dependence parameter estimator is superconsistent and asymptotically normal.

Method is computationally efficient and applicable to real-world spatial data.

Abstract

We introduce a new class of conditional autoregressive models for spatially dependent functional data, formulated through conditional means given neighboring functional observations and characterized by a covariance operator and a spatial dependence parameter. Our estimation strategy consists of three components: (i) estimating the covariance operator using conditionally centered data, (ii) estimating the spatial dependence parameter by maximizing the likelihood of projected observations, and (iii) applying a novel profile-based approach to obtain the final estimators. Under an expanding lattice framework, we establish two key theoretical results. First, we establish the consistency of the proposed covariance estimator, which is not attainable using naive methods based on marginally centered data. Second, we prove that the spatial dependence parameter estimator is superconsistent and asymptotically normal, where the latter property enables statistical inference for spatial dependence in functional data -- a contribution that is novel in the existing literature. Numerical studies support the theoretical results and demonstrate the computational efficiency of our method. Finally, we illustrate its practical utility by analyzing weekly PM$_{2.5}$ concentration trajectories in 2019 across counties in the Midwestern United States.