ABSTRACT Penalized regression methods that perform simultaneous model selection and estimation are ubiquitous in statistical modeling. The use of such methods is often unavoidable as manual inspection of all possible models quickly becomes intractable when there are more than a handful of predictors. However, automated methods usually fail to incorporate domain-knowledge, exploratory analyses, or other factors that might guide a more interactive model-building approach. A hybrid approach is to use penalized regression to identify a set of candidate models and then to use interactive model-building to examine this candidate set more closely. To identify a set of candidate models, we derive point and interval estimators of the probability that each model along a solution path will minimize a given model selection criterion, for example, Akaike information criterion, Bayesian information criterion (AIC, BIC), etc., conditional on the observed solution path. Then models with a high probability of selection are considered for further examination. Thus, the proposed methodology attempts to strike a balance between algorithmic modeling approaches that are computationally efficient but fail to incorporate expert knowledge, and interactive modeling approaches that are labor intensive but informed by experience, intuition, and domain knowledge. Supplementary materials for this article are available online.

中文翻译：

---

评估惩罚回归中的调整参数选择变异性

摘要 同时执行模型选择和估计的惩罚回归方法在统计建模中普遍存在。使用此类方法通常是不可避免的，因为当预测变量数量较多时，对所有可能模型的手动检查很快就会变得棘手。然而，自动化方法通常无法纳入领域知识、探索性分析或其他可能指导更具交互性的模型构建方法的因素。混合方法是使用惩罚回归来识别一组候选模型，然后使用交互式模型构建来更仔细地检查该候选集。为了识别一组候选模型，我们推导出沿解决方案路径的每个模型将最小化给定模型选择准则的概率的点估计和区间估计，例如，Akaike 信息准则、贝叶斯信息准则（AIC、BIC）等。 ，以观察到的解决方案路径为条件。然后考虑选择概率高的模型进行进一步检查。因此，所提出的方法试图在计算效率高但未能结合专家知识的算法建模方法和劳动密集型但由经验、直觉和领域知识提供信息的交互式建模方法之间取得平衡。本文的补充材料可在线获取。