Square‐Root LASSO for High‐Dimensional Sparse Linear Systems with Weakly Dependent Errors
研究了平方根LASSO在高维稀疏线性模型中的表现，推导了估计误差的渐近与非渐近界，适用于弱相依误差（如α-混合、ρ-混合等），并通过数值模拟和金融数据应用验证了方法的一致性。
We study the square‐root LASSO method for high‐dimensional sparse linear models with weakly dependent errors. The asymptotic and non‐asymptotic bounds for the estimation errors are derived. Our results cover a wide range of weakly dependent errors, including α ‐mixing, ρ ‐mixing, ϕ ‐mixing, and m ‐dependent types. Numerical simulations are conducted to show the consistency property of square‐root LASSO. An empirical application to financial data highlights the importance of the results and method.