Tuning parameter selection for penalized estimation via R2
Tuning parameter selection for penalized estimation via R2
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DOI：
10.1016/j.csda.2023.107729
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Julia Holter;Jonathan W. Stallrich
中科院分区：
文献类型：
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作者：
Julia Holter;Jonathan W. Stallrich
惩罚估计的调整参数选择策略对于识别可解释和预测的模型至关重要。然而，流行的策略（例如，通过交叉验证最小化平均平方预测误差）倾向于选择具有过多预测变量的模型。提出了一种简单而强大的交叉验证策略，该策略基于最大化观测值和预测值之间的平方相关性，而不是为了支持恢复而最小化平方误差损失。该策略可以应用于所有惩罚最小二乘估计量，并且在某些条件下，该度量隐式执行称为α-修改的偏差调整。当应用于 Lasso 估计器时，α-修改与松弛 Lasso 估计器密切相关。该方法在功能变量选择问题上进行了演示，以确定表面肌电图传感器的最佳放置以控制机器人手假肢。
The tuning parameter selection strategy for penalized estimation is crucial to identify a model that is both interpretable and predictive. However, popular strategies (e.g., minimizing average squared prediction error via cross-validation) tend to select models with more predictors than necessary. A simple yet powerful cross validation strategy is proposed which is based on maximizing the squared correlation between the observed and predicted values, rather than minimizing squared error loss for the purposes of support recovery. The strategy can be applied to all penalized least-squares estimators and, under certain conditions, the metric implicitly performs a bias adjustment named theα-modification. When applied to the Lasso estimator, theα-modification is closely related to the relaxed Lasso estimator. The approach is demonstrated on a functional variable selection problem to identify optimal placement of surface electromyogram sensors to control a robotic hand prosthesis.