选择不同的模型进行科学洞察
Selecting Diverse Models for Scientific Insight
摘要：
模型选择的目的往往是选择一个单一的模型，假设模型的形式是正确的。然而，在一组可以解释反应的预测因子中，可能存在多种可能的潜在解释模式。不考虑模型不确定性的模型选择可能无法揭示这些模式。我们探讨多模型惩罚回归（MMPR），以承认模型的不确定性的背景下，惩罚回归。我们研究了不同的惩罚设置如何促进不同模型中系数的收缩或稀疏。该方法被调整为显式地限制模型相似性。选择惩罚形式，强制变量选择适用于预测堆垛层错能（SFE）从钢合金成分。其目的是识别具有不同协变量子集的多个模型，以解释单一类型的响应。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
