面向UAR模型及其高阶UAR模型的修正的SCAD惩罚估计方法
Modified SCAD Penalty Estimation for UAR Model and Higher-Order UAR Model
摘要: 平滑剪切绝对偏差(SCAD)惩罚估计虽已应用于不确定自回归(UAR)模型，但其依赖样本信息而未纳入先验信息；同时，能融合先验信息的修正的SCAD惩罚估计方法尚未拓展至UAR模型及其高阶UAR模型。为此，本文将修正的SCAD惩罚估计方法拓展至UAR模型及其高阶UAR模型，构建了面向UAR模型及其高阶UAR模型的修正的SCAD惩罚估计方法，并通过两个数值算例分别验证其可行性与有效性。
Abstract: While the Smoothly Clipped Absolute Deviation (SCAD) penalty estimation has been applied to the Uncertain Autoregressive (UAR) model, it relies solely on sample information and fails to incorporate prior knowledge. Meanwhile, the modified SCAD penalty estimation method, which can integrate such prior information, has not yet been extended to the UAR model or its higher-order counterparts. To address this gap, this paper extends the modified SCAD penalty estimation to both the UAR model and its higher-order forms. The corresponding estimation framework is constructed, and two numerical examples are presented to respectively validate its feasibility and effectiveness.
文章引用：扶庆阳, 王钧传, 张鸿, 高林庆. 面向UAR模型及其高阶UAR模型的修正的SCAD惩罚估计方法[J]. 运筹与模糊学, 2026, 16(2): 21-30. https://doi.org/10.12677/orf.2026.162016

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