A Flexible Tool for Model Building: the Relevant Transformation of the Inputs Network Approach (RETINA)*
提出一种名为RETINA的新方法，通过非线性变换和选择性搜索构建模型，在模拟数据中能高成功率恢复数据生成过程，并应用于电话服务需求建模。
Abstract A new method, called Relevant Transformation of the Inputs Network Approach is proposed as a tool for model building. It is designed around flexibility (with nonlinear transformations of the predictors of interest), selective search within the range of possible models, out‐of‐sample forecasting ability and computational simplicity. In tests on simulated data, it shows both a high rate of successful retrieval of the data generating process, which increases with the sample size and a good performance relative to other alternative procedures. A telephone service demand model is built to show how the procedure applies on real data.