Variable Selection for Spatial Logistic Autoregressive Models

Article Geosciences, Multidisciplinary

Facing spatial massive data in science and society: Variable selection for spatial models

Romina Gonella et al.

Summary: This work focuses on variable selection for spatial regression models with irregular lattices and Conditional or Simultaneous Auto-Regressive (CAR or SAR) models for errors. The strategy is to whiten the residuals by estimating their spatial covariance matrix and then perform L1-penalized regression LASSO. The study proves the sign consistency for general dependent errors and provides conditions on the weight matrix of the SAR or CAR model to ensure the validity of the method.

SPATIAL STATISTICS (2022)

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Article Statistics & Probability

Incorporating spatial structure into inclusion probabilities for Bayesian variable selection in generalized linear models with the spike-and-slab elastic net

Justin M. Leach et al.

Summary: In this study, spatial information is incorporated into variable selection by assigning intrinsic autoregressive priors, resulting in more similar shrinkage penalties among spatially adjacent parameters. A computationally efficient coordinate-descent-based EM algorithm is used to fit the model, with applications in Alzheimer's Disease imaging data.

JOURNAL OF STATISTICAL PLANNING AND INFERENCE (2022)

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Article Computer Science, Interdisciplinary Applications

Robust variable selection with exponential squared loss for the spatial autoregressive model

Yunquan Song et al.

Summary: This study proposes a robust regression estimator based on penalized exponential squared loss for general spatial autoregressive models. The method employs an adaptive lasso penalty for simultaneous model selection and parameter estimation. Numerical studies show that the proposed method is particularly robust when dealing with outliers or intensive noise.

COMPUTATIONAL STATISTICS & DATA ANALYSIS (2021)

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Article Statistics & Probability