Lee Eun Ryung, Park Seyoung, Lee Sang Kyu, Hong Hyokyoung G
Department of Statistics, Sungkyunkwan University, Seoul, 03063, Korea.
Department of Statistics and Probability, Michigan State University, East Lansing, MI, 48823, USA.
Lifetime Data Anal. 2023 Oct;29(4):769-806. doi: 10.1007/s10985-023-09603-w. Epub 2023 Jul 2.
Despite the urgent need for an effective prediction model tailored to individual interests, existing models have mainly been developed for the mean outcome, targeting average people. Additionally, the direction and magnitude of covariates' effects on the mean outcome may not hold across different quantiles of the outcome distribution. To accommodate the heterogeneous characteristics of covariates and provide a flexible risk model, we propose a quantile forward regression model for high-dimensional survival data. Our method selects variables by maximizing the likelihood of the asymmetric Laplace distribution (ALD) and derives the final model based on the extended Bayesian Information Criterion (EBIC). We demonstrate that the proposed method enjoys a sure screening property and selection consistency. We apply it to the national health survey dataset to show the advantages of a quantile-specific prediction model. Finally, we discuss potential extensions of our approach, including the nonlinear model and the globally concerned quantile regression coefficients model.
尽管迫切需要一个针对个人兴趣的有效预测模型，但现有的模型主要是为平均结果开发的，目标是普通人群。此外，协变量对平均结果的影响方向和大小在结果分布的不同分位数上可能不成立。为了适应协变量的异质性特征并提供一个灵活的风险模型，我们为高维生存数据提出了一种分位数向前回归模型。我们的方法通过最大化非对称拉普拉斯分布（ALD）的似然性来选择变量，并基于扩展贝叶斯信息准则（EBIC）推导最终模型。我们证明了所提出的方法具有确定筛选性质和选择一致性。我们将其应用于国家健康调查数据集，以展示分位数特定预测模型的优势。最后，我们讨论了我们方法的潜在扩展，包括非线性模型和全局关注的分位数回归系数模型。
