Variable importance based interaction modelling with an application on initial spread of COVID-19 in China
提出了一种基于变量重要性的交互建模方法（VIBIM），用于线性回归中同时处理连续和分类预测变量的交互选择，并在中国COVID-19初期传播数据上展示了更好的可解释性、稳定性和预测性能。
Abstract Interaction selection for linear regression is useful in many fields of modern science, yet very challenging. Existing methods focus on finding one optimal model but they may perform poorly in terms of stability for high-dimensional data, and they do not typically deal with categorical predictors. In this paper, we introduce a variable importance based interaction modelling (VIBIM) procedure for learning interactions in a linear regression model with both continuous and categorical predictors. We apply the VIBIM procedure to a COVID-19 data and show that the VIBIM approach leads to better models in terms of interpretability, stability, reliability, and prediction.