Enhancing spatial functional linear regression with robust dimension reduction methods

doi:10.1016/j.jmva.2025.105538

Enhancing spatial functional linear regression with robust dimension reduction methods

2025-11-01

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DOI：10.1016/j.jmva.2025.105538

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This paper introduces a robust estimation strategy for the spatial functional linear regression model using dimension reduction methods, specifically functional principal component analysis (FPCA) and functional partial least squares (FPLS). These techniques are designed to address challenges associated with spatially correlated functional data, particularly the impact of outliers on parameter estimation. By projecting the infinite-dimensional functional predictor onto a finite-dimensional space defined by orthonormal basis functions and employing Mestimation to mitigate outlier effects, our approach improves the accuracy and reliability of parameter estimates in the spatial functional linear regression context. Simulation studies and empirical data analysis substantiate the effectiveness of our methods. Fisher consistency and influence function of the FPCA-based approach are established under regularity conditions. The rfsac package in 1 implements these robust estimation strategies, ensuring practical applicability for researchers and practitioners.

Keyword:

Functional partial least squares

Functional principal component analysis

M-estimation

Spatial autoregressive model

Spatial dependence

Enhancing spatial functional linear regression with robust dimension reduction methods

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