Fridge：针对个性化预测的岭回归聚焦微调
Fridge: focused fine-tuning of ridge regression for personalize predictions
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中文总结 AI 辅助
本文提出聚焦岭回归（fridge）方法，为每个个体计算独特调优参数，通过插入估计和参数自助法实现个性化预测，在模拟和真实数据上优于交叉验证岭回归，并应用于个性化医学中的风险与治疗反应预测。
中文摘要 AI 辅助
统计预测方法通常需要对一个或多个调优参数进行某种形式的微调，其中K折交叉验证是标准程序。对于岭回归，存在众多方法，但所有方法（包括交叉验证）的共同点是：为所有未来预测选择一个单一参数。我们提出为每个我们希望预测结果的个体计算一个独特的调优参数。通过关注特定个体的协变量向量，这产生了个性化的预测。聚焦岭回归（fridge）方法被引入，包含两部分贡献：1）首先，我们定义一个最小化特定协变量向量均方预测误差的预言调优参数；2）然后，我们提出通过使用回归系数和误差方差参数的插入估计来估计该调优参数。该方法通过参数自助法扩展到逻辑岭回归。对于高维数据，我们提出使用带交叉验证的岭回归作为插入估计，模拟表明，对于模拟数据和真实数据，fridge比带交叉验证的岭回归产生更小的平均预测误差。我们在两个个性化医学应用中展示了线性和逻辑回归模型的新概念：基于基因表达数据预测个体风险和治疗反应。该方法在R包“fridge”中实现。
英文摘要
Statistical prediction methods typically require some form of fine-tuning of tuning parameter(s), with $K$-fold cross-validation as the canonical procedure. For ridge regression there exist numerous procedures, but common for all, including cross-validation, is that one single parameter is chosen for all future predictions. We propose instead to calculate a unique tuning parameter for each individual for which we wish to predict an outcome. This generates an individualized prediction by focusing on the vector of covariates of a specific individual. The focused ridge -- fridge -- procedure is introduced with a two-part contribution: 1) first we define an oracle tuning parameter minimizing the mean squared prediction error of a specific covariate vector, 2) then we propose to estimate this tuning parameter by using plug-in estimates of the regression coefficients and error variance parameter. The procedure is extended to logistic ridge regression by utilizing parametric bootstrap. For high-dimensional data, we propose to use ridge regression with cross-validation as the plug-in estimate, and simulations show that fridge gives smaller average prediction error than ridge with cross-validation for both simulated and real data. We illustrate the new concept for both linear and logistic regression models in two applications of personalized medicine: predicting individual risk and treatment response based on gene expression data. The method is implemented in the R package "fridge".
发表机构
- University of Oslo（奥斯陆大学）
机构由 AI 辅助整理，请以论文原文为准。
