罚函数回归方法，如套索，是分析高维数据的流行方法。套索的一大优点是自然地进行变量选择。然而，一个重要的关注点是这些选择的可靠性。受大规模假设检验文献中的本地虚假发现率方法的启发，我们提出了一种方法，通过套索模型计算每个变量的本地虚假发现率。这些速率可以用于评估单个特征的可靠性，或者估计模型的整体虚假发现率。该方法可以用于任何正则化水平。这对于具有少数高度显著特征但高整体虚假发现率的模型特别有用，这在使用交叉验证选择模型时相对常见。它还足够灵活，可应用于许多类型的罚似然性，包括广义线性模型和Cox回归，以及各种惩罚，包括极小化凸面罚（MCP）和平滑剪辑绝对偏差（SCAD）惩罚。我们证明了这种方法的有效性，并将其与其他罚函数回归推断方法以及针对单变量假设检验的本地虚假发现率进行了对比。最后，我们通过将其应用于乳腺癌患者基因表达的案例研究中展示了我们方法的实用效用。版权所有©2023 John Wiley＆Sons有限公司。

Penalized regression methods such as the lasso are a popular approach to analyzing high-dimensional data. One attractive property of the lasso is that it naturally performs variable selection. An important area of concern, however, is the reliability of these selections. Motivated by local false discovery rate methodology from the large-scale hypothesis testing literature, we propose a method for calculating a local false discovery rate for each variable under consideration by the lasso model. These rates can be used to assess the reliability of an individual feature, or to estimate the model's overall false discovery rate. The method can be used for any level of regularization. This is particularly useful for models with a few highly significant features but a high overall false discovery rate, a relatively common occurrence when using cross validation to select a model. It is also flexible enough to be applied to many varieties of penalized likelihoods including generalized linear models and Cox regression, and a variety of penalties, including the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalty. We demonstrate the validity of this approach and contrast it with other inferential methods for penalized regression as well as with local false discovery rates for univariate hypothesis tests. Finally, we show the practical utility of our method by applying it to a case study involving gene expression in breast cancer patients.© 2023 John Wiley & Sons Ltd.