论文标题
为科学见解选择各种模型
Selecting Diverse Models for Scientific Insight
论文作者
论文摘要
假设模型的形式是正确的，模型选择通常旨在选择单个模型。但是，在一组可以解释响应的预测指标中可能存在多种可能的基本解释模式。不考虑模型不确定性的模型选择可能无法揭示这些模式。我们探索多模型的惩罚回归（MMPR），以确认惩罚回归背景下的模型不确定性。我们研究了不同的惩罚设置如何在单独模型中促进系数的收缩或稀疏性。该方法被调整为明确限制模型相似性。使用钢合金组合物预测堆叠断层能量（SFE）的惩罚形式的选择。目的是识别具有不同的协变量子集的多个模型，这些模型解释了一种类型的响应。
Model selection often aims to choose a single model, assuming that the form of the model is correct. However, there may be multiple possible underlying explanatory patterns in a set of predictors that could explain a response. Model selection without regard for model uncertainty can fail to bring these patterns to light. We explore multi-model penalized regression (MMPR) to acknowledge model uncertainty in the context of penalized regression. We examine how different penalty settings can promote either shrinkage or sparsity of coefficients in separate models. The method is tuned to explicitly limit model similarity. A choice of penalty form that enforces variable selection is applied to predict stacking fault energy (SFE) from steel alloy composition. The aim is to identify multiple models with different subsets of covariates that explain a single type of response.
