论文标题
具有多个分位数的高维数据集成
Data integration in high dimension with multiple quantiles
论文作者
论文摘要
本文介绍了来自多个来源（实验）的高维数据的分析，因此具有不同的相关响应，但共享相同的预测因子。预测因子的测量可能在实验之间有所不同。我们引入了一种具有多个分位数的新回归方法，以选择那些在任何分位数级别上影响任何响应的预测指标并估算非零参数。我们的估计器是惩罚目标函数的最小化器，该函数汇总了来自不同实验的数据。我们建立了估计量的模型选择一致性和渐近正态性。此外，我们提出了一个信息标准，该信息也可以用于一致的模型选择。仿真和两个数据应用说明了我们方法的优势，该方法将跨实验和分位数跨预测因子引起的组结构考虑在内。
This article deals with the analysis of high dimensional data that come from multiple sources (experiments) and thus have different possibly correlated responses, but share the same set of predictors. The measurements of the predictors may be different across experiments. We introduce a new regression approach with multiple quantiles to select those predictors that affect any of the responses at any quantile level and estimate the nonzero parameters. Our estimator is a minimizer of a penalized objective function, which aggregates the data from the different experiments. We establish model selection consistency and asymptotic normality of the estimator. In addition we present an information criterion, which can also be used for consistent model selection. Simulations and two data applications illustrate the advantages of our method, which takes the group structure induced by the predictors across experiments and quantile levels into account.
