A Comparative Study of Penalised, Bayesian, Spatial, and Tree-Based Models for Provincial Poverty in Indonesia: Small Samples and High Collinearity

• URL: http://arxiv.org/abs/2604.06278v1
• Date: Tue, 07 Apr 2026 09:41:12 GMT
• Title: A Comparative Study of Penalised, Bayesian, Spatial, and Tree-Based Models for Provincial Poverty in Indonesia: Small Samples and High Collinearity
• Abstract summary: This paper evaluates the provincial causes of poverty in Indonesia by addressing specific statistical hazards.<n>We employ a rigorous model-comparison framework designed for small samples with high collinearity.<n>Results demonstrate that algorithmic complexity is inherently risky in regional datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Identifying the structural drivers of poverty in regional datasets is frequently hindered by small sample sizes and high multidimensional collinearity, which can result in unstable and misleading policy advice. This paper evaluates the provincial causes of poverty in Indonesia by addressing these specific statistical hazards. We employ a rigorous model-comparison framework designed for small samples ($n=34$) with high collinearity, comparing standard linear models with frequentist penalisation, Bayesian shrinkage priors, an adjusted spatial intrinsic conditionally autoregressive (ICAR) model, and complex machine learning ensembles. To ensure a robust evaluation, we measure predictive performance using strict Leave-One-Out Cross-Validation (LOOCV). The results demonstrate that algorithmic complexity is inherently risky in regional datasets: simple linear shrinkage models (Ridge, Elastic Net, LASSO) achieve the superior out-of-sample prediction, whereas complex ensembles like BART suffer from severe overfitting. Across all successful regularised models, ICT skills consistently emerge as the most stable proxy for lower provincial poverty. The primary contribution of this paper is demonstrating that, in data-constrained regional analysis, parametrically regularised linear shrinkage provides a more reliable mathematical foundation for isolating structural development priorities, such as ICT, than either naive OLS or unconstrained machine learning.

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