Al-Momani, MarwanArashi, Mohammad2025-02-122025-02-122024-02Al-Momani, M.; Arashi, M. Ridge-Type Pretest and Shrinkage Estimation Strategies in Spatial Error Models with an Application to a Real Data Example. Mathematics 2024, 12, 390. https://DOI.org/10.3390/math12030390.2227-739010.3390/math12030390http://hdl.handle.net/2263/100749DATA AVAILABILITY STATEMENT : The dataset is accessible through the R-Package “spdep”.Spatial regression models are widely available across several disciplines, such as functional magnetic resonance imaging analysis, econometrics, and house price analysis. In nature, sparsity occurs when a limited number of factors strongly impact overall variation. Sparse covariance structures are common in spatial regression models. The spatial error model is a significant spatial regression model that focuses on the geographical dependence present in the error terms rather than the response variable. This study proposes an effective approach using the pretest and shrinkage ridge estimators for estimating the vector of regression coefficients in the spatial error mode, considering insignificant coefficients and multicollinearity among regressors. The study compares the performance of the proposed estimators with the maximum likelihood estimator and assesses their efficacy using real-world data and bootstrapping techniques for comparison purposes.en© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.Spatial error modelAsymptotic performanceBootstrapping; pretestRidge estimatorShrinkageArticle