<![CDATA[Modal regression has attracted increasing attention as an alternative to mean-based regression, particularly in settings characterized by heteroscedasticity, multimodal conditional distributions, and heavy-tailed noise. In such scenarios, estimators based on central tendency may yield predictions that fall in low-density regions of the response space. This paper proposes an adaptive k-nearest neighbor framework for modal regression that integrates entropy-guided neighborhood selection with nonparametric mode estimation, including MeanShift clustering and one-dimensional kernel density estimation. The proposed approach adjusts neighborhood size based on local uncertainty, allowing the regression model to adapt to variations in data density without relying on a globally fixed parameter. Extensive experiments on simulated datasets and real-world benchmarks demonstrate that adaptive modal regression methods generally reduce or stabilize prediction errors relative to fixed-k modal regression and classical kNN mean and median estimators, particularly under heteroscedastic and multimodal conditions, although the magnitude of improvement varies across scenarios. Statistical tests confirm significant differences in most experimental settings, with practical gains ranging from incremental to substantial depending on data complexity. In addition to accuracy, computational behavior is explicitly examined. The findings show a trade-off between computational cost and predictive robustness: entropy-guided adaptive modal regression requires additional runtime due to neighborhood adaptation and density estimation, but this overhead increases proportionally with sample size and remains manageable for medium-sized datasets. Based on these results, adaptive modal regression provides a useful and flexible alternative for regression tasks involving complex and heterogeneous data distributions where robustness is prioritized over minimal computation time.]]>
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