发表机构
Indian Institute of Technology (Indian School of Mines)（印度理工学院（印度矿业学院））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对线性混合效应面板数据模型对污染和协变量敏感的问题，提出基于最小二乘近似的自适应LASSO惩罚DPD估计方法，兼具稳健性与计算效率，模拟和SWAN骨密度数据验证了其优势。
AI 中文摘要
线性混合效应面板数据模型广泛应用于纵向生物医学、环境和社会研究中，但通常对数据污染和大量协变量敏感。为应对这些挑战，我们提出了一种基于密度功率散度（DPD）目标函数的最小二乘近似（LSA）并结合自适应LASSO惩罚的稳健变量选择方法。LSA将非线性的DPD目标转换为计算高效的二次近似，同时保留DPD估计的稳健性。在适当的正则条件下，所提出的DPD自适应LASSO-LSA估计量被证明具有oracle性质，包括选择一致性和渐近正态性。模拟研究表明，与惩罚似然方法相比，该方法具有更好的稳健性、更优的稀疏恢复能力以及显著提高的计算效率，同时保持与现有基于DPD的方法相当的性能。对SWAN骨密度数据集的应用展示了所提方法在稳健估计和可靠识别重要协变量方面的实际意义。
英文摘要
Linear mixed-effect panel data models are widely used in longitudinal biomedical, environmental, and social studies, but are often sensitive to data contamination and numerous covariates. To address these challenges, we propose a robust variable selection approach based on a least squares approximation (LSA) of the density power divergence (DPD) objective function combined with the Adaptive LASSO penalty. The LSA converts the nonlinear DPD objective into a computationally efficient quadratic approximation while preserving the robustness of DPD estimation. Under suitable regularity conditions, the proposed DPD Adaptive LASSO-LSA estimator is shown to possess oracle properties, including selection consistency and asymptotic normality. Simulation studies demonstrate improved robustness, better sparsity recovery, and significantly enhanced computational efficiency than penalized likelihood methods, while maintaining performance comparable to existing DPD-based approaches. An application to the SWAN bone mineral density dataset illustrates the practical relevance of the proposed methodology for robust estimation and reliable identification of important covariates.
Comments81 pages, 10 figures
