A new class of functional conditional autoregressive models
提出了一类新的条件自回归模型，用于处理空间相关的函数型数据，通过条件均值公式化，并发展了包含协方差算子估计、空间依赖参数估计的估计策略，证明了估计量的一致性和超一致性，可用于PM2.5浓度轨迹分析。
Summary 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 PM2.5 concentration trajectories in 2019 across counties in the Midwestern United States.